The ubiquitous problem of electrical energy waste is driven by human error, in which people regularly forget to turn off appliances when they leave a room, resulting in excessive consumption. The paper presents an energy-efficient TRIZ-inspired (EETRIZ) paradigm to automate consumer electronic devices in order to address this problem. Employing motion detection at entry and exit points, the system discerns room occupancy, effectively managing electrical appliances like lamps, fans, and air conditioners. The integration of theory of inventive problem solving (TRIZ) principles guides the ideation and design of an efficient switching mechanism. The system demonstrates that it is flexible by controlling multiple electronic consumer devices simultaneously. This paradigm can be used in a variety of environments, such as homes, offices, schools, and universities, and its goal is to reduce excessive energy use that results from human negligence. With an emphasis on economic viability and environmental impact, the proposed EETRIZ paradigm not only offers enhanced energy efficiency but also lays the groundwork for long-term sustainability practices, aligning with global efforts towards greener and smarter energy management. The proposed EETRIZ is implemented on C platform with support of MPLAB, Nuvoton 8051 Serie MCU Programming, GMPLibrary (GMP-5.1.1) and, Miracl Library. The proposed EETRIZ outperforms current state-of-the-art methods in terms of energy efficiency, cost-effectiveness, precision, and user-friendliness, according to hardware testing.

This research review paper comprehensively assesses the environmental impacts of diverse energy sources and proposes sustainable solutions. Fossil fuels contribute to pollution, habitat destruction, and greenhouse gas emissions. Renewable energy offers cleaner alternatives but faces challenges in manufacturing and disposal. Nuclear power has low emissions but poses risks in waste management and radiation. Biomass can be sustainable if responsibly sourced, but unsustainable practices lead to deforestation and emissions. Solutions include renewable technologies, energy efficiency, carbon capture, and sustainable transportation. Effective policies, incentives, and individual actions are crucial for a greener future. This review highlights the urgency of addressing energy-related environmental impacts and offers valuable insights for policymakers and individuals to take decisive actions for a sustainable energy future that protects the environment and promotes well- being.

Transportation is the primary consumer of oil, accounting for a significant portion of global energy consumption (34%) and CO2 emissions (40%). Electrifying road transportation is crucial to mitigate the effects of global warming. The abundant potential of renewable energy sources like solar and wind is hindered by their intermittent nature and incongruity with peak energy demands. This calls for the development of sophisticated energy storage solutions. Metal-air batteries (MABs), emerging as viable alternatives to lithium-ion batteries (LIBs), have attracted considerable attention due to their promising applications in the transportation sector. Despite substantial progress in MAB development over the past two decades, overcoming critical challenges such as electrolyte decomposition, carbon cathode degradation, anodic dendrite growth, and air impurities remains essential for their commercial viability. This mini-review provides a comprehensive overview of MAB fundamentals and the challenges associated with their development. The insights presented in this review serve to illuminate the current landscape and future prospects of MABs in the transportation sector.

This review focuses on the Just Energy Transition (JET) policies of the BRICS member countries, with the aim of finding lessons and possible adoption of some of the key energy policy aspects utilized in other countries for benchmarking for the South African context. Considerations of the present stage of JET in South Africa and the energy-source mix supporting the electricity sector, cognisant of the lifespan and condition of power plants. Analysis of the energy mix revealed that all nations were energy dependent on electricity which was produced by predominantly fossil fuelled power plants with high GHG (concentrating on high CO2 emissions). It was concluded that some of the learnings and lessons learnt from the BRICS countries might be incorporated into a South African energy plan for the transition with options and guidance for formulation of policies. This study will attract responsible environmentally conscious audience that has a commitment and drive to combat global warming and curbing climate change.

Commercial development of tidal stream energy is hampered by technical and financial challenges, and impeded by uncertainty about potential environmental effects that drive environmental risk assessments and permitting (consenting) processes. The effect of greatest concern for operational tidal stream energy devices is the potential for marine animals to collide with turbine blades, resulting in injury or death. Due to the turbulent and often turbid waters that frequently characterize tidal turbine sites, there is an absence of empirical evidence about collisions with marine animals. This paucity of observations often leads to risk-averse permitting decisions that further restrict the deployment of tidal energy devices that are needed to collect this evidence. This paper relies on the framework of stressors and receptors that is used widely in marine energy studies and outlines a stepwise probabilistic methodology that applies existing knowledge to further elucidate the risk to marine animals from operational tidal turbines. A case study using striped bass from the Bay of Fundy, Canada, accompanies the methodology, to partially demonstrate its application.

The IPCC’s sixth assessment report projects 15% to 43% (44 EJ/y – 310 EJ/y) of global primary energy to be generated by biomass in 2050 across multiple GHG mitigation scenarios. That report also emphasises the importance of electrification to meet GHG reduction targets. With increased reliance on electric power, and increased appeal to biomass, bioenergy for electricity is expected to play a major role in future energy markets. What makes the bioenergy solution more attractive is its reported reasonable Energy Return on Investment (EROI). However, generation at large scale is projected to be greatly dependent on crops and plantations. This shifts the GHG emissions concern to be concerns over land use and other emissions integrated in the bioenergy lifecycle. It is therefore vital to know whether the potential of electricity generation from biomass outweighs environmental impact of bioenergy. This paper evaluates the potential of biomass electricity mainly generated from short rotation woody crops combustion in generating green energy. This is done using the “Green EROI (EROIg)” quantification methodology, which indicates the net energy generated to society after investing in ecosystem maintenance energy (ESME). ESME is a non-monetary weighting mechanism of an entity’s different lifecycle environmental impacts. This study found that the EROIg of bioelectricity is marginally larger than unity when converted to its primary equivalent form (EROIg-PE) which indicates that the technology is somewhat energetically viable if its production was to be green. Three design options were proposed to improve bioenergy’s EROIg performance, these include adding 20% waste wood in the combustion mix, staggered harvesting and plantation to achieve annual harvest and pelletizing wood. This approach appeared to improve the EROIg especially for pelletizing, due to its simultaneous reduction in storage and transport costs, making the production energetically and environmentally viable even at a 1 : 1 secondary : primary ratio with an EROIg of 1.11 and an EROIg-PE of 3.17. We conclude with the discussion of the multiple indirect advantages of growing crops that can be used for energy generation, and a discussion on how this technique can be used alongside others to help them generate cleaner energy while facing the current global climate, biodiversity and waste issues.

In hydraulic systems, energy dissipation can be significant. The pressure drops that can occur in the hydraulic circuit, influenced by the adopted drive architecture, result in an absorbed power often significantly greater than that required by the mechanical system. In this paper, a comparative study of energy efficiency among five drive common architectures in industrial hydraulic axes is carried out. The analysis is applied to a hydraulic blanking press with variable speed and force, a fairly frequent industrial system, e.g. in the production of semi-finished brass products. Standard, regenerative, high-low, variable displacement pump and variable speed drive for a fixed displacement pump configurations have been analysed and compared. An adequate and optimized sizing of the various components of the system has been carried out in each case and subsequently the energy consumption has been estimated for a load cycle common to all the considered cases. The results show that the choice of power generation architecture of the hydraulic system has a very significant impact on energy efficiency and consequently operating costs and carbon footprint. The performed quantification of the potential energy efficiency of the considered drive architectures can be very useful in guiding energy-conscious choices.

The economic downturn caused by the financial crisis of 2008-2009 and the intensifying glob-al climate policy trends forced changes in the energy management of the European Union. The study examined how the most relevant energy economic variables affected the economic growth of the EU between 2010-2019. The study examined the relationship between GDP (dependent variable) and energy consumption, greenhouse gas emissions, the average energy price, and renewable energy use (independent variables) using the PSL-PM methodology. The results show an asymmetric picture because the independent variables influenced the eco-nomic growth of the EU in the examined period with different correlations and with different strengths. One of the significances of the study is that the presented and analyzed variables, as well as the results of the relationships between them, can contribute to the optimization of the EU's currently critical energy management and economic growth.

Development of methods and algorithms to predict the effect of mutations on protein stability, protein-protein, and protein-DNA/RNA binding is necessitated by the needs of protein engineering and for understanding the molecular mechanism of disease-causing variants. The vast majority of the leading methods are either methods with adjustable parameters or machine learning algorithms, both requiring a database of experimentally measured folding and binding free energy changes. These databases are collections of experimental data taken from scientific investigations typically aimed at probing the role of particular residue on the above-mentioned thermodynamics characteristics, i.e., the mutations are not introduced at random and do not necessarily represent mutations originating from single nucleotide variant (SNV). Thus, the reported performance of the leading algorithms assessed on these databases or other limited cases, may not be applicable for predicting the effect of SNVs seen in the human population. Indeed, we demonstrate that the SNVs and non-SNVs are not equally presented in the corresponding databases and the distribution of the free energy changes are not the same. Furthermore, the Pearson correlation coefficients (PCCs) obtained on cases involving SNVs are less impressive than for non-SNVs, indicating that caution should be used in applying them to reveal the effect of human SNVs. All methods are found to underestimate the energy changes by roughly a factor of 2.

Data centres, though a necessary part of modern society, are being stigmatised for consuming vast amounts of electricity for their operational and cooling needs. Due to Ireland’s reliance on fossil fuels to meet the increased energy demand of data centres, the data centres are contributing significantly to Ireland’s total carbon emissions. As much of this energy is expelled from data centres as waste heat energy, the potential for recycling some of this wasted heat energy was explored using environmentally friendly systems from recent publications. The recovered waste heat energy was applied in a vertical farming system, and the benefits of this waste heat to the vertical farm were analysed and quantified in two scenarios. Using conservative estimates, it was predicted that each vertical farm could be between 5-23% the size of the data centre and produce enough food to feed between 14-61 adults their daily calorie needs, and between 13-58 people their daily fresh produce requirements, depending on the scenario applied. For a more accurate prediction, each vertical farm would have to be assessed on a case-by-case basis. However, there was not enough data available on Irish data centres to perform these calculations.

This paper proposes how to reduce the consumption of active electrical energy (kwh) by 90.3% while maintaining the same mechanical work speed (RPM) at variable torque loads (fans or air-fluid blowers, centrifugal pumps are not included). of water and similar fluids), by using the “Fan Law” in an innovative way in PMSM-type synchronous motors (a comparative study never before carried out on the Fan Law). The work is carried out comparatively between brushless a-synchronous motors with starting loop (or motor with a short-circuited loop) versus brushed a-synchronous motors and PMSM-type synchronous motors without the need to use VDF (variable frequency drives), simplifying technology (electronics) and saving costs in an innovative way (R+D+i). The case study was developed on a design applied to a centrifugal air extractor/blower with PMSM/IPM type synchronous motor. Applying one of the fan affinity laws –with the impeller diameter 10.5 (mm) constant- the electrical power absorbed by the blower motor is proportional to the cube of the shaft speed: . Being “P” power (Watts) and “N” speed (RPM). Carrying out a comparative study between power (watts), active energy consumption (kwh) and rotational speed (RPM). This has a direct impact on the costs of residential and commercial single-phase active electrical energy consumption, measured in kilowatt-hours (kwh). Carrying out a comparative study between three (3) types of alternating current (AC) electrical machines, according to the NEMA (National Electrical Manufacturers Association); AC motors fall into three (3) categories. One (1), synchronous motors (Syncrhonous Motor) of three types: (1a) excitation by DC (DC Excited Motor), (1b) permanent magnet (Permanent Magnet Motor) and (1c) reluctance motor (Reluctance Motor) or motor Step by Step. Two (2), asynchronous induction motors of two types: (2a) Squirrel-Cage Induction Motor and (2b) Wound-Rotor Induction Motor. Three (3) series-wound motor (Series-Wound Motor) also called universal motor (they have carbons).

We investigate the performance of a novel flat photovoltaic-thermal (PV-T) module under high-vacuum through a 1D numerical model based on steady-state energy balance, with the aims of optimizing the simultaneous production of thermal and electrical energy. In the proposed design, the photovoltaic (PV) cell is positioned directly above the selective solar absorber (SSA), in a multilayer or fully integrated PV-SSA structure, which allows full exploitation of spectral solar radiation. In fact, in this configuration the losses related to non-absorption of low-energy photons and thermalization, typical of a classical single-junction PV cell, are reduced. The present study is conducted as the emittance and energy bandgap of the PV layer varied, thus admitting a wide variety of materials into the analysis. The dependence of the temperature coefficient, β(%/K), on the energy bandgap of the PV cell is also included. In the last part of the work, we discuss the performance of the proposed evacuated PV-T equipped with a SSA layer and thin film solar cells, namely those made of CdTe, CdS and GaAs. Overall, the paper highlights the great advantage of using high vacuum insulation, which suppresses conductive losses, and the versatility of the proposed system, which could be adapted to the user's needs simply by choosing the appropriate material for the photovoltaic layer.

This article is devoted to a bibliometric analysis of IEEE Energy Internet and Energy Systems Integration Conference proceedings posted in IEEE Xplore in 2017-2021. The main objective of the study was to identify the actual research issues on the Energy Internet topic. Author Keywords, INSPEC Controlled Terms, and terms compiled from n-grams derived from conference papers title and abstract texts were used to describe the actual issues. The terms were clustered using VOSviewer, and the terms included in each cluster were treated as a description of the actual issues. Brief descriptions of several publications relevant to the issue in question were given for task detail. The actual issue, which in my opinion deserves further, more detailed analysis, is proposed.

Steel fibers (SF) and polyolefin (PF) are widely used for shotcrete, especially in tunnelling. Both SF and PF and have shown to meet the standards in order to reduce or substitute the conventional steel mesh as reinforcement of concrete showing mechanical and productive advantages. This study sought the analysis and assessment of the mechanical properties of fiber reinforced shotcrete, comparing in-situ with laboratory results. This was performed with low and high fiber dosages of SF and PF as well as a combination of them (hybrid mixtures). A total of seven mixtures, two of steel, two of polyolefins, two hybrids and a control mixture were manufactured at laboratory and in situ. By performing tests according to EN 14651 and slabs according to EN 14488-5, it was possible to characterize the residual flexural strengths (fRj), and energy absorption capacity (E25mm) in slabs. In addition, a fracture surface analysis was performed, and the orientation factor was obtained. This allowed the authors to quantify the loss of residual strength on site and correlate it with the fiber content and positioning. Moreover, some relationships between beam and slab tests were found, allowing to correlate the most relevant type of tests in the field of fiber reinforced concrete.

The construction field uses up over one-third of the global energy consumption and contribute to 40% of CO2 emissions according to the International Energy Agency (IEA) and the 2020 annual reporter of United Nation, Goal 11 (Make cities inclusive, safe, resilient and sustainable) which discusses sustainable, safe and efficient buildings. Therefore, Morocco has a commitment to this program by publishing the law 47-09 of energy efficiency. This work aims to study the energy efficiency of two types of building, a conventional and a natural building. Conventional building is constructed using concrete, while the natural one uses sand clay and straws. As for the technique of making the natural building, it perpetually follows the same approach accustomed in rural zones of Atlas Mountains in Morocco. In this research we also simulate, temperature and humidity variation inside these buildings using TRNSYS software. Sketch Up software was employed to design these houses. The weather database is used for a typical meteorological year (TMY). In the case of natural building, many building configurations were simulated: roof insulation, floor insulation, different types of glazing and sun protection. What's more, the thermal comfort is revealed to be more conspicuous in the case of natural building.

The energy transition requires integration of different energy carriers, including electricity, heat, and transport sectors. Energy modeling methods and tools are essential to provide a clear insight into the energy transition. However, the methodologies often overlook the details of small-scale energy systems. The study states an innovative approach to facilitate sub-national energy systems with 100% renewable penetration and sectoral integration. An optimization model, OSeEM-SN, is developed under the Oemof framework. The model is validated using the case study of Schleswig-Holstein. The study assumes three scenarios representing 25%, 50%, and 100% of the total available biomass potentials. OSeEM-SN reaches feasible solutions without additional offshore wind investment, indicating that they can be reserved for supplying other states’ energy demand. The annual investment cost varies between 1.02 bn – 1.44 bn €/yr for the three scenarios. The electricity generation decreases by 17%, indicating that with high biomass-based combined heat and power plants, the curtailment from other renewable plants can be decreased. Ground source heat pumps dominate the heat mix; however, their installation decreases by 28% as the biomass penetrates fully into the energy mix. The validation confirms OSeEM-SN as a beneficial tool to examine different scenarios for sub-national energy systems.

Growing energy demand in the distributed energy system (DES) besides new charging loads of electric vehicles (EVs) make DES infrastructure unavailable earlier than normal projections. DES lines need to be upgraded to carry the modern demand of customers. DES line upgrade (LU) cost minimized and deferred to the next years by using energy storage system (ESS). ESS offers the opportunity to use energy flexibly in the distribution grid. It is possible to use ESS to shift the inevitable upgrade costs of the distribution grid elements and increase the power quality of the distribution grid. In this study, a method is proposed to extend the lifespan of DES with ESS and reduce line upgrade and investment costs in the distribution system. For this purpose, the IEEE 33 bus test grid is used, and the proposed method is tested and analyzed with different case studies. According to the results, the proposed method can reduce the investment cost by up to 80%. Besides, ESS usage considering load increase and line conditions of the distribution grid, upgrade costs of distribution grid are shifted, resulting in an optimal dimensioning and positioning while extending the lifespan of the distribution grid elements and, at the same time, offering significant improvements in energy quality. Additionally, it is seen that the power losses of the distribution grid are reduced up to 26%, and the voltage profile of buses is improved with the usage of ESS.

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. This study reviews the state-of-the-art ML applications in the biofuels’ life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx, CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

Machine learning (ML) is penetrating in all walks of life and is one of the major driving forces behind the fourth industrial revolution, typically known as Industry 4.0. The purpose of the present study is to review the state-of-the-art ML applications in the biofuels' life cycle stages, i.e., soil, feedstock, production, consumption, and emissions. A keyword search is performed to retrieve relevant articles from the databases of the Web of Science and Google Scholar. ML applications in the soil stage were mostly based on the use of satellite images of land for estimation of biofuels yield or suitability analysis of agricultural land. In the second stage of the life cycle, assessment of rheological properties of the feedstocks and their effect on the quality of biofuels were dominant studies reported in the literature. The production stage included estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage included analysis of engine performance and estimation of emissions temperature and composition, such as NOx CO, and CO2. This study identified the following trends: dominant ML method, the stage of life cycle getting more usage of ML, the type of data used for the development of the ML-based models, and the stage-wise frequently used input and output variables. The findings of this article are beneficial for academia and industry-related people involved in model development in different stages of biofuel’s life cycle.

The cutting-edge photovoltaic cells are an indispensable part of the ongoing progress of earth-friendly plans for daily life energy consumption. However, the continuous electrical demand that extends to the night time requires a prior deployment of efficient real-time storage systems. In this regard, metal-air batteries have presented themselves as the most suitable candidates for solar energy storage, combining extra lightweight with higher power outputs and promises of longer life cycles. Scientific research over non-precious functional catalysts has always been the milestone and still contributing significantly to exploring new advanced materials and moderating the cost of both complementary technologies. Metal-organic frameworks (MOFs) derived functional materials have found their way to the application as storage and conversion materials, owing to their structural variety, porous advantages, as well as the tunability and high reactivity. In this review, we provide a detailed overview of the latest progress of MOF-based materials operating in metal-air batteries and photovoltaic cells.

According the JKR theory of adhesive contact, changes of the contact configuration after formation of the adhesive neck and before detaching are completely reversible. This means, that after formation of the initial contact, the force-distance dependencies should coincide, independently on the direction of the process (indentation or pull-off). In the majority of real systems, this invariance is not observed. The reasons for this may be either plastic deformation in the contacting bodies or surface roughness. One further mechanism of irreversibility (and corresponding energy dissipation) may be chemical heterogeneity of the contact interface leading to the spatial dependence of the specific work of adhesion. In the present paper, this "chemical" mechanism is analyzed on a simple example of an axisymmetric contact (with axisymmetric heterogeneity). It is shown that in the asymptotic case of a "microscopic heterogeneity", the system follows, during both indentation and pull-off, JKR curves, however, corresponding to different specific surface energies. After the turning point of the movement, the contact area first does not change and the transition from one JKR curve to the other occurs via a linear dependency of the force on indentation depth. The macroscopic behavior is not sensitive to the absolute and relative widths of the regions with different surface energy but depends mainly on the values of the specific surface energy.

Because of the significant enabling role smart meters can play in securing the transition towards sustainable energy distribution, the paper provides insights to support smart meters implementation projects. Energy utilities must propose adequate solutions to manage grid-upgrading projects and, in the meantime, increase efficiency levels. Based on empirical data analysis the paper provides insights aimed at maximize probability of success of smart meters projects. Results show common patterns of variables that may support project undertakers, policymakers and scholar when it comes to analyze projects to predict to maximize opportunities. For smart meters projects to succeed, regulatory stability is essential as long-period investments grids produce benefits for energy utilities, and for society.

The sensor nodes have limited computation, sensing, communication capabilities and generally operated by batteries in a harsh atmosphere with non-replenish able power sources. These limitations force the sensor network subject to failure because most of the energy is spent on sensing, computing and data transmission. This paper introduces an Energy Efficient Clustering and Shortest-Path Routing Protocol (EECSRP) to assist Wireless Sensor Networks (WSNs) by (a) extending the lifespan of the network (b) effectively using the battery power (c) decreasing the network overhead and (d) ensuring a high packet transmission ratio with minimal delay. The delay time-based Cluster Head (CH) is elected based on the node degree, residual energy and Received Signal Strength (RSS) to accomplish the goal. Additionally, the RSS-based network partitioning is implemented to evaluate the gradient based on demand routing between source (sensing node) and destination (BS). Whenever the current CH residual energy goes under the threshold level, the proposed protocol performs the clustering process, reducing the exchange of control packets. However, the BS periodically gathers the data from every single CH which helps to reduce the collision and Medium Access Control (MAC) layer conflict. From the simulation results, it is the evident that the proposed protocol performance in terms of average end-to-end latency, packet delivery ratio, average energy consumption and control overhead is better than the well-known current protocols.

A lack of trust in Energy Service Company (ESCo) is the most critical factor affecting the development of Energy Performance Contracting (EPC) in China compared with other constraints. One cannot easily estimate the energy-saving performance of an EPC project. Under that condition, lack of trust may cause the Energy-Consuming Unit (ECU) to suspect the energy-saving performance promised by the ESCo, thus leaving potentially profitable projects without necessary funding. Currently, specific studies taking an across-projects viewpoint on annual energy-saving performance of EPC projects in multiple subsectors objectively and quantitatively are lacking. This paper studies the regression relationships of annual energy-saving quantity in terms of revamping cost and the regression relationships of annual cost saving in terms of revamping cost. The regression results show that there are statistically significant correlations in the above relationships in the nine subsectors investigated. This is significant for ESCos and ECUs because knowledge on energy-saving performance could contribute to EPC investment decisions and trust relationships between ESCos and ECUs. Then a multiple linear regression model of revamping cost is set up to analyze its influencing factors. The model indicates that the subsector the sample belongs to, financing, registered capital of the ESCo, and contract period have significant effects on revamping cost. Thus, policy implications regarding innovation of EE promotion technology, clarifying ESCos’ exit mechanism, innovation of financing mechanism, and improving the market credit environment for promoting investment in EPC projects are provided.

Clausius’ virial theorem set a basis for relating kinetic energy in a body of independent material particles to its potential energy, pointing to their complementary role with respect to the second law of maximum entropy. In action mechanics, expressing the entropy of ideal gases as a capacity factor for sensible heat or enthalpy plus the configurational work to sustain the relative translational, rotational and vibrational action yields algorithms for estimating chemical reaction rates and positions of equilibrium. All properties of state including entropy, work potential as Helmholtz and Gibbs energies and activated transition state reaction rates can be estimated, using easily accessible molecular properties, such as atomic weights, bond lengths, moments of inertia and vibrational frequencies. Understanding how Clausius’ virial theorem balances the internal kinetic energy with field potential energy justifies partitioning between thermal and statistical properties of entropy, yielding a more complete view of the evolutionary nature of the second law of thermodynamics. The ease of performing these operations is illustrated by three important chemical gas phase reactions, the reversible dissociation of the hydrogen molecules, lysis of water to hydrogen and oxygen and the reversible formation of ammonia from nitrogen and hydrogen. Employing the ergal also introduced by Clausius to define the reversible internal work to overcome molecular interactions plus the configurational internal work of negative Gibbs energy as a function of volume or pressure may provide a practical guide for managing risk in industrial processes and climate change at the global scale.

Poland is the 3^rdproducer of mushrooms in the world. Mushroom production in Poland accounts for nearly 25% of the total production in the EU, and it is still growing. One type of waste generated during mushroom production is mushroom spent compost (MSC), with a 5:1 (MSC: mushrooms) production rate. We investigated valorizing the MSC to produce fuel via torrefaction (‘roasting’, a.k.a. low-temperature pyrolysis). Specifically, we developed models for the MSC torrefaction kinetics using thermogravimetric analyses (TGA) and the effects of torrefaction temperature (200~300 °C) and process duration time (20~60 min) on the resulting biochar (fuel) properties. The estimated activation energy value of MSC torrefaction was 22.3 kJ^.mol^-1. The highest higher heating value(HHV) = 17.9 MJ^.kg^-1d.m. was found for 280 °C (60 min torrefaction time). The temperature of torrefaction significantly (p<0.05) increased the HHVfor constant process duration. The torrefaction duration time significantly (p<0.05) increased the HHVfor 220 °C and decreased HHVfor 300 °C. The highest mass yield 98.5% was found for 220 °C (60 min), while the highest energy yield was found for 280 °C (60 min). In addition, estimations of the value (€132.3·Mg^-1d.m. or 27.7 €·Mg^-1w.m) and quantity of resulting biochar (from torrefied MSC with 65.3% moisture content) were made based on the 280°C (60 min) torrefaction variant, assuming the price of commercially available coal fuel. We have shown a concept for an alternative utilization of abundant biowaste (MSC). The initial economic evaluation showed that MSC torrefaction might be profitable. This research provides a basis for alternative use of an abundant biowaste and can help charting improved, sustainable mushroom production.

An off-grid solar energy storage system (ESS) in National Pingtung University of Science and Technology (NPUST) was built and officially operated on Jun. 16th 2022. The system is installed in a 40' general container with PV panels of solar power 8250 W_p on top of the container. The ESS is made by repurposed lithium iron phosphate (LFP) batteries of 20 kWh capacity, where a battery management system (BMS) is adopted to ensured the safety of the battery system. An energy management system (EMS) is built to receive, process, analyse, store, and output the energy information. In this manuscript, a brief engineering report about the system is given.

This paper explores the latest developments in electrolysis technology, a key player in the transition to sustainable energy systems. Electrolysis, despite currently contributing a small share to global hydrogen production, holds immense potential for producing green hydrogen. The study delves into the efficiency of electrolysis systems, emphasizing ongoing efforts to enhance energy conversion rates. It investigates the impact of high-temperature electrolysis on reducing electricity consumption, thus making the process more efficient. The paper discusses the various challenges in the research on water electrolysis and underscores the critical role electrolysis plays in integrating renewable energy sources. The study emphasizes the need for continuous advancements in electrolysis technology to bridge existing gaps, making a compelling case for its pivotal role in the green hydrogen revolution.

Many Caribbean island nations have historically been heavily dependent on imported fossil fuels for both power and transportation, while at the same time being at an enhanced risk from the impacts of climate change, although their emissions represent a very tiny fraction of the global total responsible for climate change. Small island developing states (SIDS) are among the leaders in advocating for the ambitious 1.5°C Paris Agreement target and the transition to 100% sustainable, renewable energy systems. In this work we present three central results. First, we show through GIS mapping of all Caribbean islands the potential for near-coastal deep-water as a resource for Ocean Thermal Energy Conversion (OTEC) and couple these results with an estimate of the countries for which OTEC would be most advantageous due to a lack of other dispatchable renewable power options. Second, hourly data have been utilized to explicitly show the trade-offs between battery storage needs and dispatchable renewable sources such as OTEC in 100% renewable electricity systems, both in technological and economic terms. Finally, the utility of near-shore, open-cycle OTEC with accompanying desalination is shown to enable a higher penetration of renewable energy and lead to lower system levelized costs than those of a conventional fossil fuel system.

This study presents the development and implementation of a remote monitoring system for a dual-axis solar tracker, integrating Industry 4.0 technologies such as IoT, SCADA, and MQTT. The system, which combines a Siemens S7-1200 PLC, an IoT gateway, and a SCADA platform based on Ignition, demonstrated significant increases in solar capture efficiency, with production gains of 25-30% compared to fixed systems, reaching up to 67.65% under ideal conditions. In addition to improving operational management through predictive monitoring and early fault detection, the study identified areas for improvement, such as the need for real-world testing and the integration of weather predictions. In conclusion, the research validates the feasibility and effectiveness of automated IoT and SCADA-based systems to optimize solar trackers, significantly contributing to improved energy efficiency in photovoltaic installations.

The amount of solar energy produced is directly proportional to the surface area of solar panels exposed to sun rays. To increase the productivity of solar panels, a number of techniques are proposed by curving the surface or protruding the panel surface to maximise the surface area, e.g. Protruding panel surfaces with shapes such as pyramidal, cone, spherical cap, sinusoidal wavy curve, etc. These techniques are analysed to determine whether the panel surface area increases. The geometrical design analysis showed that, depending on the chosen surface curving method, the panel can achieve a remarkable increase in surface area.The system advisor model was used to assess a sample of solar cells with standard measurements of 6 by 6 inches that were organised in a zigzag pattern with different angles of inclination (10°, 15°, 20°, 25°, 30°, and 35°). Overall, the maximum power pump and annual AC energy performance increase by 1.4%, 4.2%, 6.8%, 10.8%, 14.9%, and 21.6%, respectively, over a one-year period of productivity. However, by applying the concept of solar panel efficiency degradation due to tilting solar panels at an angle by Mamun et al. (2022), for each 5° increase in tilt angle beyond the ideal 10°, the efficiency decreases by approximately 0.76%. In conclusion, the overall efficiency is greater than that of standard flat solar panels, provided the tilt angle is within a reasonable range.

It’s very well known that the world's attention is currently focused on the energy transition. The energy security, in addition to the drive to reduce greenhouse gas emissions in order to limit global warming, and the generalization of the access to energy, have contributed to adoption of the Moroccan Energy Strategy with a strong focus on Renewable Energies (RE). Morocco is notoriously poor in conventional fossil primary energy resources with an energy dependence on the order of 90%. The energy crisis and that resulted from Covid-19 pandemic and the geopolitical conflicts, combined compounded with steady increase in demand have heavily affected the security and the stability of the country's energy situation. The transition to RE, by strongly engaging in the implementation of several solar, wind and hydro energy projects benefiting from the country's excellent solar and wind energy potential, has made the country the leader in RE in Africa. By 2030, the share of RE in the installed capacity is expected to reach 52%. In this work, an overview of the current situation of RE (particularly solar energy) in Morocco is provided, including, the potentials, obstacles and challenges, and future perspectives. Thanks to its high solar potential location, it’s predictable that Morocco effort will be focused on this field: the Erasmus Plus Innomed Project is a virtuous example of international cooperation, aiming at create a Solar Energy Network, in synergy with EU Partners.

The accurate forecasting of solar power output is essential for ensuring the stable and efficient operation of grid-connected photovoltaic (PV) systems, especially when faced with variable weather patterns. This research focuses on the integration of Machine Learning (ML) methodologies and climatic parameters to predict solar panel energy generation, with a specific emphasis on addressing consumption-production imbalances. Leveraging a dataset sourced from the Kaggle platform, the study is conducted in the context of Estonia, aiming to optimize solar energy utilization in this geographic region. The dataset, obtained from Kaggle, encompasses comprehensive information on climatic variables, including sunlight intensity, temperature, and humidity, alongside corresponding solar panel energy output. Leveraging machine learning algorithms, including XGBoost regression and neural networks, our model aims to identify complex patterns and relationships within the datasets. By tailoring the model to Estonia's climatic nuances, we seek to enhance the accuracy of energy production forecasts and, consequently, better manage the challenges associated with consumption-production imbalances. Furthermore, the research investigates the adaptability of the proposed model to diverse climatic conditions, ensuring its applicability for similar endeavors in other geographical locations. The performance of the forecasting model will be evaluated using error metrics like mean absolute error (MAE) and root mean squared error (RMSE). This study leverages a comprehensive dataset from Kaggle and utilizes advanced machine learning techniques to generate valuable insights. These insights can inform the development of sustainable energy policies and practices, ultimately leading to a more efficient and reliable renewable energy infrastructure.

In 2023, residential and commercial sectors together consumed approximately 27.6% of total U.S. energy, equivalent to about 20.6 quadrillion Btu. Factoring in the electrical system energy losses, the residential sector represented approximately 19.7% of total U.S. energy consumption during that time. There were approximately 144 million housing units in the United States in 2023, which is increasing yearly. In this study, information on energy usage in the United States (U.S.) residential sector has been analyzed and then represented as energy intensities to establish benchmark data and to compare energy consumption of varying sizes and locations. First, public sources were identified, and data from these previously published sources were aggregated to determine the energy use of the residential sector within the US. Next, as part of this study, the energy data for seven houses/apartments from five different United States climate zones were collected firsthand. That data was analyzed, and the energy intensity of each home was calculated and then compared with the energy intensities of the other homes in the same states using RECS data. The energy intensity for each facility was calculated based on the actual energy bills. Finally, the study evaluated the carbon footprint associated with residential energy consumption in all 50 states to reinforce the importance of sustainable development initiatives.

This paper investigates the theoretical foundation for developing renewable geothermal resources locally. For this reason, we pay attention to the role of communities in geothermal development. This study presents arguments for a framework that approaches the geothermal resources as an endogenous factor of community development. To analyse it, we create a model that explains the local economic characters of the geothermal exploitation beyond its geological conditions. It aims to conceptualise a community based geothermal development standard referring to the endogeneity principle. Geothermal energy is given attention since the characteristics of this resource determine its use locally. We argue that the role of communities in geothermal exploitation is pivotal in the process of green growth for further expansion of geothermal energy use.

This paper offers a theoretical and experimental examination of the concurrent production of electricity and heat using photovoltaic thermal (PV/T) technology. The efficiency performance of the PV/T system is meticulously analyzed using MATLAB/Simulink software environments. Notably, the proposed PV/T system shows reliable performance, and its validity is confirmed through experimental validation on a test stand with a single PV/T panel positioned at a 45-degree angle to the horizontal and a 0-degree azimuth angle.

Amidst the recent energy crisis, the pivotal role of resource efficiency and renewable energy sources (RES) for sustainable development has become apparent. Transition to sustainability involves decentralized energy solutions empowering local communities to generate, store, and utilize their energy, diminishing reliance on centralized systems and potentially transforming them into power flexibility resources. This is particularly crucial in non-interconnected island systems that primarily rely on imported oil for electricity generation. Such reliance not only implies high production costs but also threatens energy security and adds to the environmental footprint of local economic activities. Addressing the above necessitates, amongst other elements, the adoption of advanced energy management with sophisticated forecasting and demand-side management (DSM) strategies. In response, we introduce a versatile algorithm investigating the impact of DSM at the community scale, designed to maximize the utilization of renewable energy produced from local installations. Integrated as an ancillary module in an advanced energy management platform, the algorithm undergoes testing using datasets collected from end-consumers and a small-scale RES installation on the island of Tilos, Greece. This study not only offers insights for energy stakeholders but also establishes theoretical parameters that can inform subsequent decision-making processes.

We introduce quantum spatio-temporal dynamics (QSD) as modeled by the Nexus Paradigm (NP) of quantum gravity to resolve the problem of energy- momentum localization in a gravitational field. Currently, the gravitational field as described using the language of geometry modeled under General Relativity (GR) fails to provide a generally accepted definition of energy-momentum. Attempts at resolving this problem using geometric methods have resulted in various energy-momentum complexes whose physical meaning remain dubious since the resulting complexes are non-tensorial under a general coordinate transformation. In QSD, the tangential manifold is the affine connection field in which energy-momentum localization is readily defined. We also discover that the positive mass condition is a natural consequence of quantization and that dark energy is a Higgs like field with negative energy density everywhere. Finally, energy-momentum localization in quantum gravity shows that a free falling object will experience large vacuum fluctuations (uncertainties in location) in strong gravity than in weak gravity and that the amplitudes of these oscillations defines the energy of the free falling object.

The growing adoption of distributed energy production technologies, and the potential of energy underutilization when it is produced by non-connected groups, has raised interest in ‘sharing economy’ concepts in the electricity sector. We suggest that mechanisms, such as peer-to-peer (P2P) energy trading, will allow users to exchange their surplus energy for mutual benefits, stimulate the adoption of renewable energy, encourage communities to ‘democratically’ control their own energy supplies for local development, and improved energy efficiency, and create many other benefits This approach is receiving increasing attention across the world, particularly in Germany, the Netherlands and Australia. Nevertheless, the actual development and implementation of these platforms is slow and mostly limited to trial activities. This study investigates the challenges and barriers facing P2P energy trading developments based on previous academic and industry studies. We provide a comprehensive multidimensional barrier analysis through a PESTLE approach to assess the barriers from a variety of perspectives, including the political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) aspects. This approach clarifies the many intersecting problem fields of P2P trading in renewable energy, and the paper identifies a list of such barriers, and discusses the prospects for addressing these issues. We also elaborate on the importance of incentive-based P2P market design.

The surprisingly high catalytic activity of gold has been known to the heterogeneous catalysis community since the mid-1980s. Significant effort has been directed towards improving the reactivity of these surfaces towards important industrial reactions. One such strategy is the introduction of small amounts of other metals to create Au-based surface alloys. In this work, we investigate the synergistic effect of Pt doping of the Au(111) surface towards decreasing the activation barrier of the methanol dehydrogenation elementary step, within first-principles density functional theory. To this aim we construct several models of \ce{Pt}-doped Au(111) surfaces, including a full \ce{Pt} overlayer and monolayer. The effect of \ce{Pt} surface doping is then investigated via the computation of adsorption energies of the various chemical species involved in the catalytic step, and the estimation of activation barriers of methanol dehydrogenation. Both electronic and strain effects induced by \ce{Pt} surface doping concur in substantially lowering the activation energy barrier of this important reaction elementary step. Morever, in the presence of preadsorbed atomic oxygen, \ce{Pt} surface doping can be used to reduce the activation energy for methanol dehydrogenation to as low as 0.1 eV.

The relative proportions between nutrients affects the fate of them for metabolic interconversion, storage and turnover or, essentially for energy, in a process of partition modulated by hormonal and metabolic factors. A basic common chow model was used: ST or standard diet, HF high-fat and HP or high-protein had a common substrate. A CF cafeteria-type diet was added for comparison. Rats (female and male) received the diet for 30d. Nutrient intake and body composition were measured. Plasma glucose, lactate, testosterone (T) and estradiol (E2) were measured. Common groups energy intake, was largely based in carbohydrates CH, yielding 6C units, which were fully oxidized or stored as 2C fragments (fatty acids). Lactate (3C) was partially stored as glycerol of TAG. Accrual of 2C far exceeded about ten-fold that of 3C. Amino acid (AA) catabolism yielded 2C, 3C and 4C-5C fragments, the latter acting as Krebs cycle anaplerotic intermediates (KCAI), that facilitated the oxidation of 2C in detriment of TAG accumulation Lactate levels, were related to lactic dehydrogenase activities in muscle and liver. TAG accrual was maximal in CF, quite differently from HF with similar diet lipid proportion. Maximal rates of lipid storage were found in ST (and CF) groups. Females showed a higher ability to oxidize CH and to store less TAG. T increased N and protein deposition, whilst E2 decreased them favoring AA oxidation and the availability of KCAI. In the rat, an effective energy partition scheme requires that about half of the energy is provided by CH (polysaccharide), and needs enough protein to provide KCAI for a smooth 2C oxidation.

Currently, the district heating (DH) in Poland is facing many challenges. The business model used hitherto was very simple—in most cities, heating plants produced heat, and centralized heating systems supplied the "product" at a price approved annually by the president of the Energy Regulatory Office (URE). However, recent years have brought significant changes on the market. EU regulations force the elimination of old coal-fired plants that were still built in the Polish People's Republic (PRL), moreover, high prices of CO2 emission allowances aggravate the financial situation of companies. In addition, in the heating sector, the trend observed in the power sector is becoming increasingly visible—limiting the role of large sources in favor of energy generated locally, closer to the customer. One of the biggest challenges is achieving the targets set by the EU for the share of renewable energy sources (RES) in the heating sector. The war in Ukraine and problems with the supply of coal and gas are an additional impulse to turn to RES. Thus, the development of sustainable and innovative solutions for energy production and supply at the level of urban networks is currently one of the main technical challenges. The purpose of the paper is to present the current situation and perspectives of development of district heating systems in Poland with a view to the status of district heating in the world, and some deeper insight into European conditions. The review presents energy sources with particular emphasis on renewable energy sources (RES) and their cogeneration for heat production. The examples of existing heating network solutions using renewable energy sources, based on the selected published case studies, are also discussed.

Well-intentioned regulations to protect Canada’s most productive farmland restrict large-scale so-lar photovoltaic (PV) development. The recent innovation of agrivoltaics, which is the co-development of land for both PV and agriculture, makes these regulations obsolete. Burgeoning agrivoltaics research has shown agricultural benefits including increased yield for a wide range of crops, plant protection from excess solar energy and hail, improved water conservation while maintaining agricultural employment and local food supplies. In addition, the renewable electricity generation decreases greenhouse gas emissions while increasing farm revenue. As Canada in general, and Ontario in particular, is at a strategic disadvantage in agricultural without agrivoltaics, this study investigates the policy changes necessary to capitalize on the benefits of using agrivoltaics in Ontario. Land use policies in Ontario are reviewed. Then, three case studies (peppers, sweet corn and winter wheat) are analyzed for agrivoltaic potential in Ontario. These results are analyzed in conjunction with potential policies that would continue to protect the green-belt of the Golden Horseshoe, while enabling agrivoltaics in Ontario. Four agrivoltaic policy areas are discussed: increased research and development, enhanced education/public awareness, mechanisms to support Canada’s farmers converting to agrivoltaics and using agrivoltaics as a potential source of trade surplus with the U.S.

The energy potential of long-period oscillations is estimated by comparing it with watercourse power. The relaxation time of long-period waves is chosen for the estimation time interval, during which their amplitude decreases e (Euler's number) times from the initial one. According to calculations, the amount of energy produced during this time by the watercourse is 9.35–18.71 million kW×h, while the amount of energy of long-period oscillations is 3–6 times less – 1.60–5.48 million kW×h. The components of the economic factor of using long-period waves and currents for electricity production are the predictability of their magnitudes and location of maxima, long-term availability, concentration.

Microgrid is one of the promising green transition technologies that will provide enormous benefit to the seaport, as a solution to the major concerns in this sector, namely energy crisis, economical and environmental pollution. However, finest design of the microgrid is a challenging task considering different objectives, constraints and uncertainties involved. To ensure the optimal operation of the system, determining the right configuration framework and size for each component in the seaport microgrid at the minimum cost is a vital decision at the design stage. This paper aims to design a hybrid system of seaport microgrid with optimally sized component .The selected case study is the Port of Aalborg, Denmark. The proposed grid-connected structure consists of renewable energy sources (photovoltaic system and wind turbines), an energy storage system and cold ironing as seaport’ loads. The architecture is then optimized by utilizing HOMER to meet the maximum load demand by considering a few parameters such as solar global horizontal irradiance, temperature and wind resources. Then, the best configuration framework is analyzed in terms of economic feasibility, energy reliability and environmental impact.

The last half of the 20th century was characterized by the most impactful ecosystem changes throughout regulations and guidelines minimalizing environmental harms. Many of them emphasize the importance of enterprise sustainability for renewable energy policy. When dealing with numerous alternatives for the implementation of renewables-based policy, decision-makers need to analyze their impacts on environmental, economic, and social dimensions from a long-term perspective. The aim of this paper is to highlight a challenge to apply the multi-criteria sustainability decision-making framework (MDCA) to mainstream energy policy. The integrative framework covers an approach for the needs of analyzing factors influencing the energy technology selection. In this study, the MDCA-approach combines lifecycle-based methods, Analytical Hierarchy Process, PESTEL (Political, Economic, Social, Technological, Environment and Legal). To demonstrate the applicability and usability of the method three photovoltaic technologies are assessed. The results showed that string ribbon technology is the most sustainable along its life cycle, with a 0.503 sustainability score. The study highlighted the need to embody robust methodologies assessing sustainability-oriented technologies, not used anymore for supporting energy policies. This challenge regarding example evidence can give energy decision-makers a tool to realize their energy policy and in consequence, enhance enterprise sustainability.

The concept of DC power distribution has gained interest within the research community in the past years; especially due to rapid prevalence of solar PVs as a tool for distributed generation in DC microgrids. Various efficiency analyses have been presented for the DC distribution paradigm, in comparison to the AC counterpart, considering a variety of scenarios. However, even after a number of such comparative efficiency studies, there seems to be a disparity in the results of research efforts - wherein a definite verdict is still unavailable: 'Is DC distribution a more efficient choice as compared to the conventional AC system?' A final verdict is absent primarily due to conflicting results. In this regard, system modeling and the assumptions made in different studies play a significant role in affecting the results of the study. The current paper is an attempt to critically observe the modeling and assumptions used in the efficiency studies related to the DC distribution system. Several research efforts will be analyzed for their approach towards the system upon which they have performed efficiency studies. Subsequently, the paper aims to propose a model that may alleviate the shortcomings in earlier research efforts and be able to give a definite verdict regarding the comparative efficiency of DC and AC networks for residential power distribution.

Large office buildings are responsible for a substantial portion of energy consumption in urban districts. However, thorough assessments regarding the Nordic countries are still lacking. In this paper we analyse the largest dataset to date for a Nordic office building, by considering a case study located in Stockholm, Sweden, that is occupied by nearly a thousand employees.Distinguishing the lighting and occupants’ appliances energy use from heating and cooling, we can estimate the impact of occupancy without any schedule data. A standard frequentist analysis is compared with Bayesian inference, and the according regression formulas are listed in tables that are easy to implement into building performance simulations (BPS). Monthly as well as seasonal correlations are addressed, showing the critical importance of occupancy.A simple method, grounded on the power drain measurements aimed at generating boundary conditions for the BPS, is also introduced; it shows how, for this type of data and number of occupants, no more complexities are needed in order to obtain reliable predictions. For an average year, we overestimate the measured cumulative consumption by only 4.7%. The model can be easily generalised to a variety of datasets.

Almost fifty years after the first installations, I identify the main lessons learned from fighting drought and poverty in Africa with direct solar-powered pumps thanks to Father Bernard Verspieren and Mali Aqua Viva. Six main findings and three main recommendations emerge from the present analysis. They are of direct relevance to all Africa’s countries whose population has gone from 438 million in 1977 to 1,308 million in 2019, with about 600 million still having no access to electricity. In place of “awareness campaigns” and extraordinary courses held by international organizations, I recommend to establish national solar energy institutes whose task will include the education of solar energy professionals giving practice-oriented workshops on solar-powered drip irrigation and rainwater harvesting throughout each Africa’s country. Said education will critically include the economic and social aspects of distributed “generation” of energy and water from sunlight and rainfall.

Over the last two decades, residential buildings have accounted for nearly 50 percent of total energy use in New Zealand. In order to reduce household energy use, the factors that influence energy use should be continuously monitored and managed. Building researchers and professionals have made efforts to investigate the factors that affect energy use. However, few have concentrated on the association between household energy use and the cost of residential buildings. This study examined the correlation between household energy use and residential building cost. Analysis of the correlation between energy use data and residential building cost indicated that residential building cost in the construction phase and energy use in the operation stage were significantly correlated. These findings suggest that correct monitoring of building costs can help to identify trends in energy use. Therefore, this study proposes a time series model for forecasting residential building costs of five categories of residential building (one-story house, two-story house, townhouse, apartment, retirement village) in New Zealand. The primary contribution of this paper is the identification of the close correlation between household energy use and residential building costs and provide a new area for optimize energy management.

Although many devices have recently been proposed for pressure regulation and energy harvesting in water distribution and transport networks, very few applications are still documented in the scientific literature. A new in-line Banki turbine with positive outflow pressure and a mobile regulating flap, named PRS, was installed and tested in a real water transport network for pressure and discharge regulation. The PRS turbine was directly connected to a 55 kW asynchronous generator with variable rotational velocity, coupled to an inverter. The start-up tests showed how automatic adjustment of the flap position and the impeller velocity variation are able to change the characteristic curve of the PRS according to the flow delivered by the water manager or to the pressure set-point assigned downstream or upstream of the system, still keeping good efficiency values in hydropower production.

Solar green roofs, namely rooftops functionalized with properly selected living vegetation and photovoltaic modules, achieve an ideal symbiotic relationship in which promotion of biodiversity and onsite renewable energy production are both enhanced whereas the roof provides a wide range of environmental, health, aesthetic and economic benefits. This study provides a unified outlook of this eminent sustainable technology at the dawn of its uptake across the world, especially in polluted urban areas.

Considering the need to reduce greenhouse gas emissions, onshore wind energy is certain to play a major role in future energy systems. This topic has received significant attention from the research community, producing many estimations of Europe's onshore wind potential for capacity and generation. Despite this focus, previous estimates have relied on distribution assumptions and simulation schemes that summarily under predict both the amount of available future wind capacity as well as its performance. Foremost in this regard is the common use of contemporary, or at least near-future, turbine designs which are not fitting for a far-future context. To fulfill this role, an improved, transparent, and fully reproducible work flow is presented for determining European onshore wind potential. Within a scenario of turbine cost and design in 2050, 13.5 TWof capacity is found to be available, allowing for 34.4 PWh of generation. By sorting the explicitly-placed potential generation locations by their expected generation cost, national relations between turbine cost and performance versus a desired capacity are exposed. In this way, it is shown that all countries possess some potential for onshore wind energy generation below 4 €_ct kWh^-1. and, furthermore, that it is unlikely for these costs to exceed 6 €_ct kWh^-1.

The research field of energy system analysis is dealing with increasingly complex energy systems and their respective challenges. Moreover, the requirement for open science has become a focal point of public interest. Both drivers have triggered the development of a broad range of (open) energy models and frameworks in recent years. However, there are hardly any approaches on how to evaluate these tools in terms of their capabilities to tackle energy system modelling challenges. This paper describes a first step towards a flexible evaluation of software to model energy systems. We propose a qualitative approach as an useful supplementary to existing model fact sheets and transparency checklists. We demonstrate the applicability by evaluating the newly developed “Open Energy Modelling Framework” with respect to existing challenges in energy system modelling. The case study results highlight that challenges related to complexity and scientific standards can be tackled to a large extent while the challenges of model utilization and interdisciplinary modelling are only tackled partially. However, the challenge of uncertainty remains for the most part unaddressed at present. Advantages of the evaluation approach lie in its simplicity, flexibility and transferability to other tools. Disadvantages mostly stem from its qualitative nature. Our analysis reveals that some challenges in the field of energy system modelling cannot be addressed by a software as they are on meta level like model result communication and interdisciplinary modelling.

This paper proposes an optimization method for the scheduling of a multi-energy virtual power plant supply-demand balance in the power market environment of Jiangxi Province. The objective of this method is to enhance the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method comprehensively considers the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Additionally, real-time market price feedback is integrated to achieve accurate allocation of power supply and demand. Through a case study of a multi-energy virtual power plant in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development. One of the solutions seen to ensure energy security, expand energy access and promote a low carbon future in this isolated islands is the use of renewable energy sources. This study wishes to determine the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 9-year on-site wind speed data to develop high resolution wave energy model using SWAN wave model, and assessed its annual energy production through matching with wave energy devices. Results shows that average significant wave height (Hs), peak period (Tp) and wave power density (Pd) during northeast monsoon are Hs = 1.35 m, Tp = 4.79 s and Pd = 4.05 kW/m respectively, while southwest monsoon which is sheltered by the mainland resulted to a lesser outcome, Hs = 0.52 m, Tp = 3.37 s and Pd = 0.34 kW/m. While the simulated model was observed to overestimate the wave energy resource (Bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the observed values (average r = 0.9). Its annual energy production is highest at Station 5, with AEPWaveBouy = 43.761 MWh, AEP-Pelamis = 216.786 MWh and AEPWave Dragon = 2462.66 MWh. At present, the minimum requirement for a wave energy development to be feasible is 5 kW/m, which in this case, Cuyo Island falls short, but with the continuous evolution of wave energy converters, applications on milder re-sources will soon materialized.

The adhesion of two-component lipid vesicles to a flat substrate is investigated. The components are characterized by different spontaneous curvatures. The shape of the vesicles is coupled to the lateral distribution of membrane components. The influence of the adhesion on the shape transformations and the lateral distribution of membrane components is investigated. The evolution of the vesicle’s shape and the lateral distribution of membrane components in the process of vesicle adhesion is studied. It has been shown that vesicle adhesion may induce mixing or demixing of membrane components. The change of the vesicle shape due to adhesion facilitates the curvature driven segregation of the membrane components and results in the formation of large membrane domains with various mean curvatures.

The central of this article is to present a hypothesis that can solve the long-standing question of how ball lightning arises.

The article examines the evolution and resilience of the Spanish desalination industry in response to changing market conditions and technological challenges. Initially focused on domestic projects, Spanish companies leveraged the A.G.U.A. Program to gain expertise, which later facilitated their successful international expansion as local demand diminished. The industry has adapted to varying international regulations and local conditions, demonstrating flexibility and competitiveness. In recent years, there has been a growing emphasis on integrating renewable energy into desalination processes to address rising energy costs and environmental concerns. This shift not only reduces CO2 emissions but also stabilizes operational costs, highlighting the sector’s ongoing innovation and adaptability.

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2022, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. Utilizing stepwise regression, robust least squares, Fully Modified Least Squares (FMOLS), Canonical Cointegrating Regression (CCR), and various cointegration tests including the Johansen cointegration test, Engle-Granger cointegration test, and Vector Error Correction (VEC) model, the study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle-Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman's efforts towards sustainable development and decarbonization.

A platform for indoor monitoring inside buildings, integrating both conventional and environmentally friendly devices with energy-harvesting sources, is proposed. Biomaterials such as gelatin and chitosan, derived from renewable resources, have been utilized to fabricate hydrogel and active layers for sensors and supercapacitors. These devices enhance the environmental profile of the proposed solution by employing sustainable materials and optimizing energy consumption. The developed electronic board prototype provides a versatile platform for testing various sensor configurations while accommodating different energy-harvesting sources. The article details the design of an energy harvesting system for indoor monitoring, covering various aspects regarding energy sources, power management circuits, and low-power microcontroller units. It examines energy storage devices and sensors, including both eco-friendly and commercial ones, as well as radio transceivers with different communication technologies. Additionally, an energy analysis to evaluate the performance and energy efficiency of the platform is presented.

This study uses the Scopus and Web of Science databases to review the forecast of electricity consumption from 2015 to 2024. Based on the keywords used in the article title, which are associated with electricity consumption forecasts, the study retrieved 821 docu-ments for additional analysis using various techniques. For frequency analysis, we used Mi-crosoft Excel; for data visualisation, we used VOSviewer; and for citation metrics and analysis, we used Harzing's Publish or Perish. Standard bibliometric variables, including publication growth, authorship patterns, collaboration, prolific authors, national contribution, most active institutions, favourite journals, and highly cited articles, are used in this study's reporting of the findings. Our analysis indicates that over ten years, starting in 2015, publications on the forecasting of electricity consumption have continued to increase. China was the country that contributed the most to research on electricity consumption. Most of the research publications on electricity consumption were published in the Journal of Energy.

This Paper delves into a thorough analysis of the DC-DC buck converter tailored for renewable energy applications. Given the prevalent utilization of renewable resources producing DC voltages, this converter plays a pivotal role in stepping down high-level DC voltages to more manageable levels. The research focuses on varying the duty cycle to assess its impact on the converter's output voltage performance. Simulations conducted using PSIM are meticulously compared with hardware implementation results, demonstrating the converter's efficacy in generating significantly reduced output voltages. The findings underscore the practical applicability of this converter in renewable energy scenarios, offering a succinct yet insightful perspective on its performance and adaptability.

Energy harvesting is an emerging topic recently. This harvested energy is used to operate wireless sensors for different industrial monitoring and automation. To maximize the harvested energy in spinning systems, the harvester should vibrate at resonance for different spinning frequencies which can be achieved by active fitting. In this research, a novel methodology was developed by actively customizing the length of vibrating element beam to manipulate its natural frequency according to the spinning frequency. Different models were investigated numerically and experimentally to verify the proposed methodology. COMSOL Multiphysics software V5.1 was used to develop the numerical model, then and tip deformation and voltage difference results were benchmarked. The numerical model was verified experimentally by attaching a piezoelectric MIDE PPA 1021 which its vibrating length was varied by changing the supporting length from 0 mm to 12 mm to a spinning object having variable frequency from 0-200 Hz. It is found that the MIDE PPA 1021 beam vibrated at its resonance throughout the specified spinning frequencies. The proposed harvester can be applied in battery-free sensors being used in automotives, wind-mills blades, and rotating machinery.

Greenflation or inflation for green energy transition in Europe, it becomes a structural problem of new scarcity and poverty, because the Green Deal and its fiscal and monetary policies. In this paper, it is paid attention to the Green financial bubble and the European greenflation paradox: in order to achieve greater future social wellbeing, due to a looming climate risk, present wellbeing and wealth is reduced, causing a real and ongoing risk of social impoverishment. According to the European Union data, it is going to show the relations between green transition (emissions, tax, debt, credit boom, etc.), GDP and inflation. As many emissions are reduced, there is a decrease of GDP (once deflated) and GDP per capita, evidencing social deflation, which in turn means more widespread poverty and a reduction of middle-class. Also, there is a risk of a green-bubble, like in the Great Recession of 2008 (but this time supported by the European Union) and possible stagflation. To analyze this problem, it is used here some econometric tools, but under the theoretical support of heterodox approaches, especially the Austrian Economics and the New-Institutional Schools.

The article aims to present an alternative perspective on the relativistic expression of energy. This perspective is based on the virtual use of a Dempster mass spectrometer. In this spectrometer, it is possible to simulate Alfred Bucherer's experiment and conclude, using Lienard's relativistic equation, that the classical expression of energy predicted by Special Relativity is a more complete expression than the one presented by high school and college physics textbooks.

Greenhouses are instrumental in the advancement of regions globally. The geometric arrangement of these structures plays a pivotal role in governing sunlight distribution, facilitating ventilation, and managing condensation. The roof's shape significantly affects energy efficiency and the accumulation of condensation water, which, when dripping onto crops, can induce diseases and diminish production. This study introduces a Matlab program designed for defining and analyzing greenhouse roof geometry, adaptable to both single-span and multi-span structures. Various roof shapes are examined, determining angles along their length to facilitate condensation droplet runoff. Furthermore, the greenhouse's energy efficiency is evaluated by analyzing diverse roof models, accounting for surface area and internal air volume. The results aim to aid in selecting the optimal greenhouse type based on climate and latitude. This study offers a valuable decision-making tool for the planning and design of agricultural structures, providing insights to enhance overall sustainability and performance in diverse environmental contexts.

The accumulation of waste polymer in ecosystems is a problem that affects the environment on a global scale. This paper presents the design of Pwave+, a microwave pyrolysis system that has been optimized to recycle waste polymer into valuable hydrocarbon fuels and chemicals. Heating in a microwave oven is both volumetric and rapid, which improves the effectiveness of pyrolysis and the quality of the product. The production of waste polymer oil and gas products was boosted to its full potential by a continuous microwave vortex reactor. The product gases allowed for complete independence in terms of energy production. Modeling, simulation, and experimentation with advanced technology are used to optimize the pyrolysis parameters and the performance of the reactor. As a result, simulation and programming are solutions to problems. The results of this study demonstrated both the technical viability and the long-term viability of continuous microwave pyrolysis as a method for recycling mixed polymer waste streams. The Pwave+ continuous microwave vortex reactor is described in this study. It is capable of converting various polymer waste streams into hydrocarbon products as well as energy. The use of microwaves to assist in the pyrolysis process has the potential to cut down on plastic pollution and the amount of waste that is dumped in landfills. This could provide valuable insights for the design and commercialization of similar systems.

The surging electricity demand driven by a growing global population, coupled with the increasing electrification of industries, transportation, and power-intensive appliances, poses a substantial challenge to electricity systems worldwide. This challenge is further compounded by the pressing issue of climate change. To address climate change and mitigate the environmental consequences of fossil fuel use, renewable energy sources are expected to play a pivotal role. However, certain renewable sources, such as wind and solar, exhibit stochastic behavior, resulting in unpredictable power output and significant fluctuations. This intermittency presents a challenge in maintaining a stable and reliable power supply. To overcome this challenge and ensure a balanced electricity grid, the strategic deployment of energy storage devices throughout the power system becomes critically important. Energy storage technologies play a vital role in mitigating the mismatch between renewable power generation and consumption. Additionally, they enable the storage of surplus energy generated from renewable sources during periods of high production for later use during periods of low or no generation. Each technology possesses unique characteristics and applications, providing flexibility and diversity in energy storage solutions. This work provides an overview of different energy storage technologies and explores their role in establishing a sustainable power system.

A substantial body of phenomenological and theoretical work over the last few years strengthens the possibility that the vacuum energy density (VED) of the universe is dynamical, and in particular that it adopts the `running vacuum model' (RVM) form, in which the VED evolves mildly as $\delta \rho_{\rm vac}(H)\sim \nu_{\rm eff} m_{\rm Pl}^2{\cal O}\left(H^2\right)$, where $H$ is the Hubble rate and $\nu_{\rm eff}$ is a (small) free parameter. This dynamical scenario is grounded on recent studies of quantum field theory (QFT) in curved spacetime and also on string theory. It turns out that what we call the `cosmological constant', $\Lambda$, is no longer a rigid parameter but the nearly sustained value of $8\pi G(H)\rho_{\rm vac}(H)$ around (any) given epoch $H(t)$, where $G(H)$ is the gravitational coupling, which can also be very mildly running (logarithmically). Of particular interest is the possibility suggested in past works that such a running may help to cure the cosmological tensions afflicting the $\Lambda$CDM. In the current study, we reanalyze it in full and we find it becomes further buttressed. Using the modern cosmological data, namely a compilation of the latest SNIa+BAO+$H(z)$+LSS+CMB observations, we probe to which extent the RVM provides a quality fit better than the concordance $\Lambda$CDM model, paying particular emphasis on its impact on the $\sigma_8$ and $H_0$ tensions. We utilize the Einstein-Boltzmann system solver \texttt{CLASS} and the Monte Carlo sampler \texttt{MontePython} for the statistical analysis, as well as the statistical DIC criterion to compare the running vacuum against the rigid vacuum ($\nu_{\rm eff} = 0$). On fundamental grounds, $\nu_{\rm eff}$ receives contributions from all the quantized matter fields in FLRW spacetime. We show that with a tiny amount of vacuum dynamics ($|\nu_{\rm eff}|\ll 1$) the global fit can improve significantly with respect to the $\CC$CDM and the mentioned tensions may subside to inconspicuous levels.

Climate change, the scarcity of fossil fuels, technical advances in clean energy, and the volatility of crude oil prices are just a few of the factors that have prompted the world to recognize clean energy as a viable alternative to dirty energy. As part of the Paris Climate Accord of 2015, many countries agreed to change their economies to be more sensitive to climate change. Due to this Accord, which increased interest from investors and decision-makers, investments in clean energy companies have benefited [1,2]. Clean energy stocks, which are a part of the larger world of tradable reserves, might experience pricing inefficiencies. In this paper, we investigate the multifractal scaling behavior and efficiency of green finance markets, as well as traditional markets like gold, crude oil, and natural gas between January 1, 2018, and March 9, 2023, which covers periods of low volatility and financial instability (2020 and 2022 events). To test the serial dependency (autocorrelation) and the efficient market hypothesis, in its weak form, we employed the Lo and Mackinlay test and the DFA method. The empirical findings demonstrated that both periods exhibit severe multifractal and significant asymmetry, indicating that the price indices under study are not at all efficient.

This paper describes a stochastic clustering architecture that is used in the paper for making predictions over energy data. The design is discrete, localised optimisations based on similarity, followed by a global aggregating layer, which can be compared with the recent random neural network designs, for example. The topic relates to the IDEAS Smart Home Energy Project, where a client-side Artificial Intelligence component can predict energy consumption for appliances. The proposed data model is essentially a look-up table of the key energy bands that each appliance would use. Each band represents a level of consumption by the appliance. This table can replace disaggregation from more complicated methods, usually constructed from probability theory, for example. Results show that the table can accurately disaggregate a single source to a set of appliances, because each appliance has quite a unique energy footprint. As part of predicting energy consumption, the model could possibly reduce costs by 50% and more than that if the proposed schedules are also included. The hyper-grid has been changed to consider rows as single units, making it more tractable. A second case study considers wind power patterns, where the grid optimises over the dataset columns in a self-similar way to the rows, allowing for some level of feature analysis.

Glycine in its neutral form can exist in the gas phase while its zwitterion form is more stable in water solution.But how many waters are actually necessary to stabilize the zwitterionic structure in the gas phase? Are the intramolecular isotropic spin spin coupling constants sensitive enough to accuse the change in the environment? or the conformer observed? These and related questions have been investigated by a computational study at the level of density functional theory employing the B3LYP functional and the 6-31++G**-J basis set. We found that at least two water molecules explicitly accounted in the super-molecule structure are necessary to stabilize both conformers of glycine within a water polarizable continuum model. At least half of the SSCC’s of both conformers are very stable to changes in the environment and at least four of them differ significantly between Neutral and Zwitterion conformation.

Evidence is growing that mankind must learn from nature, a self-sufficient and self-organized system that adopts all the opportunities to develop life and ingeniously makes the most of whatever energy source. Attempting to satisfy the requirements of our energy-consuming world, we cannot afford to disregard any available source of energy, mainly those characterized by zero-CO₂ emissions. In this context an alternative scenario could be opened by the use of the nuclear radiations emitted from naturally occurring or artificially produced radionuclides. Abandoned mines of U, Th and Rare Earths , as well as storage areas of artificially produced isotopes all over the globe are available and affordable sources of radiations that can be converted in electrical power. The transition from laboratory-scale nuclear batteries to large-area converting modules would allow to safely re-use a big amount of already existing radionuclides, converting a trouble into a resource.

The decarbonization of the electrical energy sector is in progress for contrasting the climate changes, with a relevant increase of the Renewable Energy Sources (RES) power plants, mostly in Dispersed Generation (DG). The adequacy and the security of power systems, with a huge penetration of RES in DG is possible with a suitable integration of energy storage. In fact, energy storages are able to provide many different services for long-term adequacy and real time security. In this framework the present paper deals with a Thermal Energy Storage (TES) proposed for power system services. The technology presented is made up of modules containing a bed of fluidizable solid particles, which can store thermal energy from waste heat, process heat and/or from electricity. Stored thermal energy can be released, e.g. as superheated steam, for thermal uses or converted into electricity, by means of steam turbines. Some possible applications are then reported explaining advantages and limits.

It has been recognized for some time that even for perfect conductors, the interaction Casimir entropy, due to quantum/thermal fluctuations, can be negative. This result was not considered problematic because it was thought that the self-entropies of the bodies would cancel this negative interaction entropy, yielding a total entropy that was positive. In fact, this cancellation seems not to occur. The positive self entropy of a perfectly conducting sphere does indeed just cancel the negative interaction entropy of a system consisting of a perfectly conducting sphere and plate, but a model with weaker coupling in general possesses a regime where negative self-entropy appears. The physical meaning of this surprising result remains obscure. In this paper we re-examine these issues, using improved physical and mathematical techniques, partly based on the Abel-Plana formula, and present numerical results for arbitrary temperatures and couplings, which exhibit the same remarkable features.

Three typhoons (Rammasun, Kalmaegi, and Sarika) travelled through the deployed stations in the northern South China Sea from 2014–2016. During the passage of typhoons, strong winds and vigorous currents resulted in horizontal displacement of buoy over 2000 m, vertical displacement of ropes on buoys as much as 200 m. The rectification can correct the warm anomaly to cool anomaly of temperature. These movements lead to biases of raw data, with temperature bias as much as 4°C, salinity as much as 0.05 psu, velocity bias as much as 0.4 m/s. The crosscheck of current velocity from different instruments shows that the bias of overlapping velocity and correlation coefficient after depth rectification obviously enhances. The observation shows that temperature cools 1.5 °C, and 0.1 psu saltier in maximum, the near-inertial current increases to 0.4 m/s in the upper layer. The inertial kinetic energy propagates downward with the upward phase, and the maximum depth can reach over 2000 m.

Energy resources are important sources of social and economic development, human survival and security. However, energy resources are objective and scarce, especially the fossil energy, such as coal, oil, natural gas, etc. Human survival environment is increasingly damaged, and the condition of energy is also gradually becoming deficient, so it is necessary to develop new energy resources to meet the increasing demand of material civilization. Solar energy can meet the needs of human development in the future as a kind of clean resource and it has been concerned by all countries in the world. We analyzed the effectiveness of subsidies to the PV industry of both the price and R&D by the spatial econometric model. In some region, the production capacity of photovoltaic industry has shown a significant spatial agglomeration situation in China since 2012. At the same time, benchmarking price and R&D subsidies played a positive incentive to the photovoltaic industry production capacity. It is a certain degree of inverse correlation between the scale of policy subsidies and the resource endowment of solar resources. Therefore, the countermeasures should be considered from the aspects of institutions, industry, technology and public financial policies.

Our primary objective is to construct a plausible unified model of inflation, dark energy and dark matter from a fundamental Lagrangian action first principle, where all fundamental ingredients are systematically dynamically generated starting from a very simple model of modified gravity interacting with a single scalar field employing the formalism of non-Riemannian spacetime volume-elements. The non-Riemannian volume element in the initial scalar field action leads to a hidden nonlinear Noether symmetry which produces energy-momentum tensor identified as a sum of a dynamically generated cosmological constant and a dust-like dark matter. The non-Riemannian volume-element in the initial Einstein-Hilbert action upon passage to the physical Einstein-frame creates dynamically a second scalar field with a non-trivial inflationary potential and with an additional interaction with the dynamically generated dark matter. The resulting Einstein-frame action describes a fully dynamically generated inflationary model coupled to dark matter. Numerical results for observables such as the scalar power spectral index and the tensor-to-scalar ratio conform to the latest 2018 PLANCK data.

This paper proposes an ARIMA approach to battery health forecasting with accuracy improvement by K shape-based clustered predictors. The health prediction of the battery pack is an important function of a battery management system in data centers. Accurate forecasting of battery life turns out to be very difficult without failure data to train a good forecasting model in real life. The conventional ARIMA model is compared with total and clustered predictors for battery health forecasting. Results show that the forecasting accuracy of the ARIMA model significantly improved by utilizing the results of the clustered predictors for 40 batteries in a real data center. One year of actual historical data of 40 batteries of large scale datacenter is presented to validate the effectiveness of the proposed methodology.

To account for the very low mass density associated with Dark Energy and the Cosmological Constant, a new approach to the ground state of empty space is presented. The resulting model for the vacuum state associated with empty space proposes a crystalline-like texture for the chromatic structure of empty space. This vacuum state has the appropriate mass density and predicts acceleration for the Universe expansion. Furthermore, the model predicts that this texture is anisotropic and may lead to measurable changes in the production of electron and positron pairs by gamma rays incident on a solid crystal of low mass density such as graphite.

An increasing amount of electric energy is consumed by computers as they progress in function and capabilities. All of it is dissipated in heat during the computing and communicating operations and we reached the point that further developments are hindered by the unbearable amount of heat produced. In this paper we briefly review the fundamental limits in energy dissipation, as imposed by the laws of physics, with specific reference to computing and memory storage activities.

This paper updates earlier thoughts by the author on a putative propulsion system. The concept was based around static electromagnetic momentum, as expounded in the “Feynman Disk” and experimentally verified by Graham and Lahoz. That said, naïve static electromagnetic momentum schemes to achieve linear translation are defeated by “hidden momentum” mechanisms, so too are simple arrangements just cycling the fields; we shall survey the flaws in their arguments. It may however be possible to achieve linear translation by means of arrangements of torques with a novel mechanism to break the symmetry of forces (or torques) on the second half of the cycle as the field is switched off. At the time of earlier presentation no mechanism could be found to explain the momentum balance for the process but it was believed that momentum was being given to the zero-point of the field. We show that it is possible to dump angular momentum and thence linear momentum to the ground state by standard quantum analysis of the EM field. None of this violates the conservation of momenergy.

Ce³⁺ and Dy³⁺ ions doped Ca₂Al₂SiO₇ (CAS) phosphors were prepared by the solid-state reaction at 12800C for 1h. X-ray diffraction patterns confirmed a tetragonal crystalline structure. The luminescent spectra of CAS: Ce³⁺, Dy³⁺ phosphors consist a board band with peaking at 420 nm corresponding to the luminescence of Ce³⁺ ions and narrow lines of Dy³⁺ ions. The energy transfer from Ce³⁺ ion to Dy³⁺ ion is presented and discussed

This study analyzes the effects of primary energy consumption and alternative energy patents on CO2 emission intensity and CO2 emissions per capita, and their relationship using canonical correlation analysis. Our results are as follows. First, CO2 emissions from natural gas and liquefied petroleum gas have positive effects, while CO2 emissions from gasoline, fuel oil, diesel, and kerosene have negative effects on CO2 emission intensity. Second, patents for devices for producing mechanical power from muscle energy have large positive effects. Finally, the more the patents use waste heat, geothermal energy, hydro energy, and wind energy, the higher the CO2 emissions from liquefied petroleum gas, gasoline, and crude oil and the lower the CO2 emissions from diesel, which are conducive to controlling CO2 emissions. Therefore, energy policies will be more effective based on the CO2 “emissions” level from primary energy consumption and the “control” degree of CO2 emissions by alternative energy.

This paper proposes an innovative postulate based on experimental observations, namely, that the speed of light can vary between two inertial reference systems. This postulate is defined as the principle of relative change in the speed of light, which refreshes the concept of absolute speed of light in traditional physics. By integrating the principle of the invariance of the speed of light with the principle of relativity in relativity theory, several important conclusions are derived: space and time must be capable of and proportionally expand and contract. Between two inertial reference systems with different relative speeds of light, objects have an energy difference of k^2 times, which induces a natural tendency for objects to move towards a state of lower energy. The acceleration is only related to the relative change rate of the speed of light in vacuum space and is independent of the mass of the object. The kinetic energy obtained by the object comes from the reduction of internal energy of the object, emphasizing the key role of energy conversion in this process. The paper provides a new theoretical perspective for explaining the physical mechanisms of universal gravitation and dark energy.

Smart grids represent the evolution of traditional electrical grids through the integration of digital and communication technologies. These grids are capable of monitoring and managing the distribution of electricity efficiently and resiliently. Artificial Intelligence (AI) plays a crucial role in this context, offering advanced solutions for optimization, forecasting, and energy management. This paper explores the main AI technologies applied to smart grids, highlighting their advantages, challenges, and impacts on energy efficiency and sustainability. The methodology employed includes a comprehensive literature review, analysis of current AI applications in smart grids, and examination of case studies to illustrate the practical implementation and benefits of AI technologies in this field.

Without an established energy management plan, human lifestyle improvement is incomprehensible. Adequate energy assets are the key to human lifestyle development, however, energy assets are restricted and exorbitant too. In this paper, an energy control system for a green environment called PMC (Power Management and Control) is proposed. The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The blend of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is utilized to make a fusion methodology to improve occupant comfort index (OCI) and decrease energy utilization. The main theme of the proposed PMC technique is to improve OCI and decrease energy utilization. The proposed framework gives better OCI when compared with its counterpart Ant Bee Colony along with Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the proposed PMC methodology gives practically the same OCI, but consumes less energy. The proposed PMC methodology additionally accomplished the most extreme OCI (i-e 1) when compared with the existing model FA-GA (i-e 0.98). The proposed PMC model consumed less energy as compared to existing models ABCKB, GAP, PSO and AEO. The proposed model consumed more energy than SOHP but provided better OCI. The comparative outcomes show the viability of the proposed PMC framework in lessening energy utilization and improving the OCI. Unlike other existing mythologies except for the AEO framework, the proposed PMC technique is additionally confirmed through a simulated climate by controlling indoor climate using actuators, like Fan, light, AC and boiler.

Inverse gas chromatography at infinite dilution was used to determine the surface thermodynamic properties of silica particles and PMMA adsorbed on silica, and more particularly, to quantify the London dispersive energy γsd, the Lewis acid γs+ and base γs- polar surface energies of PMMA/silica composites as a function of the temperature and the recovery fraction θ of PMMA. The polar acid–base surface energy γsAB and the total surface energy of the different composites were then deduced as a function of the temperature. In this paper, the Hamieh thermal model was used to quantify the surface thermodynamic energy of poly methyl methacrylate (PMMA) adsorbed on silica particles at different recovery fractions. A comparison of the new results was carried out with those obtained by applying other molecular models of the surface areas of organic molecules adsorbed on the different solid substrates. An important deviation of these molecular models from the thermal model was proved. The determination of γsd, γs+, γs-, and γsAB of PMMA in both bulk and adsorbed phases showed an important non-linearity variation of these surface parameters as a function of the temperature. The presence of maxima in the curves of γsd(T) highlighted the second order transition temperatures in PMMA showing beta-relaxation, glass transition and liquid-liquid temperatures. These three transition temperatures depended on the adsorption rate of PMMA on silica. The proposed method gave a new relation between the recovery fraction of PMMA and its London dispersive energy showing an important effect of the temperature on the surface energy parameters of adsorption of PMMA on silica. A universal equation relating γsd(T,θ) of the systems PMMA/silica to the recovery fraction and the temperature was proposed.

With the increasing demand for energy worldwide, researchers from different fields have been striving to improve the sustainability and proper utilization of energy resources. Passive radiative cooling, as a natural energy transport method, can achieve cooling without additional external energy input. This review provides a comprehensive examination of passive radiative cooling, including its fundamental theories and latest development. A particular emphasis is placed on the diverse range of fields where passive radiative cooling has been applied, notably including but not limited to construction and architecture. The current state of applications, potential challenges that may arise with wider adaption and promising research directions for each field are thoroughly discussed. This review underscores the universal applicability and feasibility of passive radiative cooling and contributes to its future development by identifying promising areas for further exploration.

This paper presents a comprehensive study on the application and optimization of automotive thermoelectric generators (ATEGs), focusing on the crucial role of heat exchangers in enhancing energy conversion efficiency. Recognizing the substantial waste of thermal energy in internal combustion engines, our research delves into the potential of TEGs to convert engine waste heat into electrical energy, thereby improving fuel efficiency and reducing environmental impact. We meticulously analyze various heat exchanger designs, assessing their influence on the TEG's output power under different exhaust gas flow rates and temperatures. Furthermore, we explore the impact of TEG material properties on the overall energy conversion effectiveness. Our findings reveal that specific heat exchanger designs significantly enhance the efficiency of waste gas heat exchange, leading to an improved performance of the TEG system. We also highlight the importance of thermal insulation in maximizing TEG output. This study not only contributes to the ongoing efforts to develop more sustainable and efficient vehicles but also provides valuable insights into the practical application of thermoelectric technology in automotive engineering. Through this research, we aim to pave the way for more environmentally friendly transportation solutions, aligning with global efforts to reduce fossil fuel dependence and mitigate environmental pollution.

Space and water heating for residential and commercial buildings amount to a third of the European Union’s total final energy consumption. Approximately 75% of the primary energy is still produced by burning fossil fuels, leading to high greenhouse gas emissions in the heating sector. Therefore, policymakers increasingly strive to trigger investments in sustainable and low-emission heating systems. This study forms part of the “Roll-out of Deep Geothermal Energy in North-West-Europe”-project and aims at quantifying the spatial heat demand distribution in the Interreg North-West-Europe region. An open-source geographic information system and selected Python packages for advanced geospatial processing, analysis, and visualization are utilized to constrain the maps. These were combined, streamlined, and optimized within the open-source Python package PyHeatDemand. Based on national and regional heat demand input data, three maps are developed to better constrain heat demand at a high spatial resolution of 100*100m2 for the residential and commercial sectors, and for both together (in total). The developed methodology cannot only be applied to transnational heat demand mapping but also on various scales ranging from city district level to states and countries. In addition, the workflow is highly flexible working with raster data, vector data, and tabular data. Results reveal a total heat demand of the Interreg North-West-Europe region of about 1,700TWh. The spatial distribution of the heat demand follows specific patterns, where heat demand peaks are usually in metropolitan regions like for the city of Paris (1,400MWh/ha), the city of Brussels (1,300MWh/ha), the London metropolitan area (520 MWh/ha), and the Rhine-Ruhr region (500 MWh/ha). The developed maps are compared with two international projects, Hotmaps and Heat Roadmap Europe’s Pan European Thermal Atlas. The average total heat demand difference from values obtained in this study to Hotmaps and Heat Roadmap Europe is 24 MWh/ha and 84 MWh/ha, respectively. It is assumed that the implementation of real consumption data is an improvement in spatial predictability. The heat demand maps are therefore predestined to provide a conceptual first overview for decision-makers and market investors. The developed methods will further allow for anticipated mandatory municipal heat demand analyses.

Transportation electrification has been identified as a crucial shift to be made for achieving energy security and emission reduction targets. Electric Vehicles (EVs) play a significant role in achieving these targets; therefore, there is a growing trend to develop new EV technologies and associated infrastructure. Along with these developments, EVs' Quality of Service (QoS) aspects should be enhanced to increase the number of EVs on the road. Various QoS factors are defined to assess the QoS of EVs, and one of the two aims of this paper is to provide a comprehensive review of these factors. The other aim is to review recent developments of associated communication technologies which are essential for exchanging information between EVs and charging stations. This paper serves as a comprehensive review and useful reference for researchers working on QoS aspects of EVs and associated communication systems aiming to enhance the QoS.

Abstract: With the continuous integration of various energy systems and the reform of energy trading marketization, the traditional power demand response can no longer meet the needs of multi-energy coupling. For the joint response problem of multi-energy, this paper proposes a comprehensive demand response optimization incentive model considering the user response characteristics, with the complete energy service providers and multiple users as the participants of the comprehensive demand response, to solve the response strategy for the optimal goal of all parties. The implementation architecture of integrated demand response and the electrothermal energy hub model is introduced first. Secondly, the integrated demand response collaborative optimization strategy of the integrated energy service providers and users is established, and the existence and uniqueness of the optimal solution are proved. Finally, the example analysis demonstrates that the proposed strategy can reduce the integrated energy service providers' response cost and user dissatisfaction under multiple scenarios.

Ever since oil was discovered in the Persian Gulf Region in the 1920s, the oil and gas industries have been dominant in the area. These countries are responsible for generating approximately 35 percent of the world's natural gas and 25 percent of its crude oil, making them the largest crude oil producers globally. The release of carbon dioxide (CO2) into the atmosphere is directly linked to the use of fossil fuels. CO2 accounts for 58.8 percent of all greenhouse gas emissions caused by human activities and has a detrimental impact on urban residents through the urban heat island effect. A significant portion of these emissions can be attributed to residential buildings. Some nations exacerbate the problem by relying excessively on fossil fuels. Oil-rich countries provide energy subsidies to the public in order to lower energy costs for their citizens. However, this policy can have negative consequences, including wasteful energy usage in countries like Iran.

Transitioning to clean cooking fuels is not only part of achieving SDG7, but also makes a significant contribution to mitigating climate change by reducing carbon emissions. The paper contradicts the conventional wisdom that electricity is too expensive to cook with. The paper explores the energy required to cook dishes using different technologies and fuels by analysing data from Cooking Diaries and Controlled Cooking tests conducted under the MECS programme. Electric pressure cookers (EPCs) use least energy when compared with electric hotplates, and a detailed description of how the EPC works explains why this should be. The paper draws out distinctions between African and Asian dishes, notably the impact of energy intensive dishes prepared mostly in Africa. Standard efficiency based approaches to comparing the performance of stoves are not appropriate to modern electric cooking devices so an alternative approach based on specific energy consumptions is proposed e.g. charcoal stoves use 15 times as much energy as EPCs to cook African dishes. Energy ratios provide a basis for estimating carbon emission reductions associated with transitioning to modern cooking fuels. Historical costs show that the cost of cooking with an EPC can be only 20% of the cost of cooking with charcoal, which highlights the potential for modern, energy efficient electric cooking devices to defy the conventional wisdom of the energy ladder.

The building and construction sector has a significant impact on CO2 emissions and pollutants released into the atmosphere, which contribute to climate change. The EPDB Directive mandates the achievement of a minimum energy class E for all residential buildings by 2030 and an energy class D by 2033. Particularly, in Italy, about 86% of the existing building stock predates the enactment of any energy laws and regulations, making it imperative to apply the energy efficiency interventions. This paper provides a support decision tool for the identification of standardized interventions on the envelope, air conditioning system and domestic hot water production. The study is focused on a specific construction period class (1976 - 1990) in six different climatic zones. The methodological approach is based on a cataloguing phase and definition of Ante Operam energy classes, on the case study identification, the energy requalification interventions identification, solutions simulations, cost estimation. By simulating standardized interventions for each climatic zone, a range of possible combinations will be identified. The most advantageous ones will be determined based on a cost-benefit analysis, considering the potential class jump achieved. The research result is a matrix of energy efficiency interventions that is applicable to each climatic zone and can be extended to the existing housing stock.

The construction industry has witnessed the increasing use of digital tools and smart solutions, particularly, in the realm of buildings energy automation within the concept of smart cities. While to truly realize the potential benefits of smart cities, a broader scope of smart initiatives is required to support the transition from smart buildings towards smart neighbourhoods, appreciated as the critical unit of urban development. To support this interplay of smart solutions between buildings and neighbourhoods, this article aims to collect and review all smart solutions presented in existing scientific articles, technical literature, and realized European projects. These solutions are classified under two main sections of buildings and neighbourhoods, which are investigated through five domains: buildings energy-related uses, renewable energy sources, water, waste, and open space management. Showcasing the quantitative outcomes demonstrates the potential benefits of implementing smart solutions from buildings to neighbourhoods. Moreover, this research concludes that the true enhancement of energy conservation goes beyond the buildings’ energy component and can be genuinely achieved by integrating intelligent elements of the neighbourhood, due to their strong interdependencies. Future research is recommended to focus on assessing the effectiveness of these solutions towards resource conservation.

The global demand for energy has been steadily increasing due to population growth, urbanization, and industrialization. Numerous researchers worldwide are striving to create precise forecasting models for predicting energy consumption to manage supply and demand effectively. In this research, a time-series forecasting model based on multivariate multilayered long short-term memory (LSTM) is proposed for forecasting energy consumption and tested using data obtained from commercial buildings in Melbourne, Australia: the Advanced Technologies Center, Advanced Manufacturing and Design Center, and Knox Innovation, Opportunity, and Sustainability Center buildings. This research specifically identifies the best forecasting method for subtropical conditions and evaluates its performance by comparing it with the most used methods at present, including LSTM, bidirectional LSTM, and linear regression. The proposed multivariate multilayered LSTM model was assessed by comparing mean average error (MAE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) values with and without labeled time. Results indicate that the proposed model exhibits optimal performance with improved precision and accuracy. Specifically, the proposed LSTM model achieved a decrease in MAE by 30%, RMSE by 25%, and MAPE by 20% compared to the LSTM method. Moreover, it outperformed the bidirectional LSTM method with a reduction in MAE by 10%, RMSE by 20%, and MAPE by 18%. Furthermore, the proposed model surpassed linear regression with a decrease in MAE by 2%, RMSE by 7%, and MAPE by 10%. These findings highlight the significant performance increase achieved by the proposed multivariate multilayered LSTM model in energy consumption forecasting.

The global trade and transportation sectors heavily rely on the maritime industry. Still, its dependence on fossil energy sources poses significant environmental challenges and leads to unstable fuel prices that affect the cost of goods transported by sea. This paper aims to evaluate the viability of seaports as energy-intensive entities and explore the feasibility of implementing a Nuclear-Renewable Hybrid Energy System (NRHES). The study presents a case study of the Tanjung Priok Port in Indonesia, focusing on estimating energy consumption, emissions, and the potential impact of carbon taxation on seaport operations. By quantifying these factors, the research provides insights into the energy requirements, environmental effects, and potential costs associated with seaport carbon taxation. A comprehensive analysis of the technical and economic feasibility of implementing an NRHES in the seaport case study is conducted, determining the optimal sizing and composition of components, considering the proportion of nuclear and renewable energy sources. Additionally, the economic analysis considers energy costs, net present cost, cash flow, return on investment, and internal rate of return. The findings aim to inform decision-makers about the benefits and challenges of adopting an NRHES, contributing to a cleaner and more sustainable future for the maritime industry.