1. Introduction

1.2. Research Objectives

The primary objective of this research is to optimize the performance of the building as a ZEB by integrating advanced solar energy systems with battery storage and behavioral analysis for predictive analysis. This study seeks to explore the following specific goals:

1. Optimization of Energy Storage Systems (ESS): The research aims to optimize the Energy Storage System (ESS) capacity within the building to enhance overall energy efficiency and minimize energy losses. This optimization process is closely aligned with the energy performance classification system developed by the Guideline for ZEB definition and evaluation by SHASE Japan [12], which categorizes buildings into four distinct zones based on energy usage reduction: ZEB Oriented Buildings (35%-50%), ZEB Ready (50%-75%), Nearly ZEB Level 2 (75%-87.5%), and Nearly ZEB Level 1 (87.5%-100%). By simulating various ESS capacities ranging from 0 to 500 kWh, the study seeks to identify the optimal capacity that will propel the building towards the highest level of energy performance, Nearly ZEB Level 1, where the building becomes nearly self-sufficient with minimal energy losses.

Figure 1. Net Zero Energy Classification from the SHASE (The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan).

Figure 1. Net Zero Energy Classification from the SHASE (The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan).

2. Impact of Solar PV System and Energy Storage Capacity: In addition to optimizing the ESS, the study examines the impact of different solar PV system capacities (160, 180, 200, and 250kW) on the building’s energy management. The SHASE classification system is also applied here to evaluate how varying the PV system size influences the building’s progress through the energy performance zones. The goal is to determine the most effective combination of solar PV capacity and ESS that will maximize energy efficiency and allow the building to achieve Nearly ZEB Level 1 status. By understanding the interaction between solar generation and storage, the research provides insights into how different system configurations can either hinder or advance the building’s journey towards optimal energy performance.

3. Behavioral Analysis: The research aggregates energy consumption data to simulate appliance usage patterns based on typical working day behaviors and appliance percentage ratios. This analysis aims to uncover critical insights into the consumption patterns of HVAC systems [13], lighting [14], and electronic devices [15], thereby enabling targeted energy-saving interventions [16].

2. Material and Methods

2.4. Method

Algorithm 1: Simulation Solar System and ESS Storage Capacity Optimization

Prepare Current Data of total energy consumption of building, 160kW Solar system and 50kW ESS. Calculate Unutilized Solar Energy Select a range of potential solar size, 160 (Ref),180,200,250kW. Select a range of potential ESS storage capacities, 0–500kWh with increments of 25kWh. Simulation System and Evaluate Performance by Zone

Figure 6. The figure compares daily data between real energy consumption (blue line) with net energy consumption (pink line) from November 1, 2023, to April 30, 2024. The real energy consumption remains consistently positive, indicating total energy use, while the pink line in the figure represents the net energy consumption for a system with a 160kW solar array and a 50 kW energy storage system (ESS) shows more variability, sometimes dipping below zero, suggesting that energy generation exceeded consumption on certain days. The dashed gray line represents the zero-energy baseline for reference.

Figure 6. The figure compares daily data between real energy consumption (blue line) with net energy consumption (pink line) from November 1, 2023, to April 30, 2024. The real energy consumption remains consistently positive, indicating total energy use, while the pink line in the figure represents the net energy consumption for a system with a 160kW solar array and a 50 kW energy storage system (ESS) shows more variability, sometimes dipping below zero, suggesting that energy generation exceeded consumption on certain days. The dashed gray line represents the zero-energy baseline for reference.

Unutilized Solar Energy (%) Calculation

Step 1: Identify Net Energy Demand

Calculate the net energy demand at each time step:

$${\mathit{E}}_{\mathit{n}\mathit{e}\mathit{t}}={\mathit{E}}_{\mathit{c}\mathit{o}\mathit{n}\mathit{s}\mathit{u}\mathit{m}\mathit{p}\mathit{t}\mathit{i}\mathit{o}\mathit{n}}-{\mathit{E}}_{\mathit{s}\mathit{o}\mathit{l}\mathit{a}\mathit{r}}$$

Where:

• ${\mathit{E}}_{\mathit{c}\mathit{o}\mathit{n}\mathit{s}\mathit{u}\mathit{m}\mathit{p}\mathit{t}\mathit{i}\mathit{o}\mathit{n}}$ is the energy consumed by the building.
• ${\mathit{E}}_{\mathit{s}\mathit{o}\mathit{l}\mathit{a}\mathit{r}}$ is the energy generated by the solar PV system.

If $\mathit{E}\_\mathit{n}\mathit{e}\mathit{t}$ is negative, it indicates that the solar system is generating more energy than the building consumes.

Step 2: Charge the Energy Storage System (ESS)

If $\mathit{E}\_\mathit{n}\mathit{e}\mathit{t}$ is negative and the ESS has available capacity, the excess solar energy can be used to charge the ESS:

$${\mathit{E}}_{\mathit{c}\mathit{h}\mathit{a}\mathit{r}\mathit{g}\mathit{e}}=\mathbf{m}\mathbf{i}\mathbf{n}\left(\left|{\mathit{E}}_{\mathit{n}\mathit{e}\mathit{t}}\right|,{\mathit{C}}_{\mathit{E}\mathit{S}\mathit{S}}-{\mathit{C}}_{\mathit{c}\mathit{u}\mathit{r}\mathit{r}\mathit{e}\mathit{n}\mathit{t}}\right)$$

Where:

• ${\mathit{E}}_{\mathit{c}\mathit{h}\mathit{a}\mathit{r}\mathit{g}\mathit{e}}$ is the amount of energy used to charge the ESS.
• ${\mathit{C}}_{\mathit{E}\mathit{S}\mathit{S}}$ is the total capacity of the ESS.
• ${\mathit{C}}_{\mathit{c}\mathit{u}\mathit{r}\mathit{r}\mathit{e}\mathit{n}\mathit{t}}$ is the current charge level of the ESS.

This step ensures that any excess solar energy is stored in the ESS until it reaches its capacity limit.

Step 3: Calculate Unutilized Solar Energy

Unutilized solar energy is the portion of excess solar energy that cannot be stored in the ESS:

$${\mathit{E}}_{\mathit{u}\mathit{n}\mathit{u}\mathit{t}\mathit{i}\mathit{l}\mathit{i}\mathit{z}\mathit{e}\mathit{d}}=\left|{\mathit{E}}_{\mathit{n}\mathit{e}\mathit{t}}\right|-{\mathit{E}}_{\mathit{c}\mathit{h}\mathit{a}\mathit{r}\mathit{g}\mathit{e}}$$

This value represents the energy that is generated but not utilized by the building or stored in the ESS.

Step 4: Sum Across Time

Perform the above calculations for each time step (e.g., hourly or daily) and sum up the unutilized solar energy over the entire period:

$$\mathit{\Sigma }{\mathit{E}}_{\mathit{u}\mathit{n}\mathit{u}\mathit{t}\mathit{i}\mathit{l}\mathit{i}\mathit{z}\mathit{e}\mathit{d}}$$

Step 5: Calculate Unutilized Solar Energy (%)

Finally, calculate the percentage of unutilized solar energy relative to the total solar energy generated:

$$\mathit{U}\mathit{n}\mathit{u}\mathit{t}\mathit{i}\mathit{l}\mathit{i}\mathit{z}\mathit{e}\mathit{d} \mathit{S}\mathit{o}\mathit{l}\mathit{a}\mathit{r} \mathit{E}\mathit{n}\mathit{e}\mathit{r}\mathit{g}\mathit{y}\left(\mathrm{\%}\right)=\left({\displaystyle \frac{\mathit{\Sigma }{\mathit{E}}_{\mathit{u}\mathit{n}\mathit{u}\mathit{t}\mathit{i}\mathit{l}\mathit{i}\mathit{z}\mathit{e}\mathit{d}}}{\mathit{\Sigma }{\mathit{E}}_{\mathit{s}\mathit{o}\mathit{l}\mathit{a}\mathit{r}}}}\right)\times 100$$

Where:

• $\sum {\mathit{E}}_{\mathit{s}\mathit{o}\mathit{l}\mathit{a}\mathit{r}}$ is the total solar energy generated over the entire period.

In the second step of the method, the data presented in the table were collected from the energy usage behavior of appliances within the ERDI building at Chiang Mai University. The data collection process involved monitoring the usage patterns of various appliances during different time periods, categorized into working hours and non-working hours, both during the day and night. The total energy consumption was analyzed by breaking it down into individual appliance contributions, such as HVAC systems, lighting, and electronics, based on predefined percentage ratios derived from historical data. The following table summarizes the behavior of each appliance group and their respective ratios of total energy consumption during working hours in the office building.

Algorithm 2: Behavioral Analysis and Appliance Ratio Method

Record typical building behaviors (e.g., working hours, occupancy). Classify appliances (HVAC, lighting, electronics) and Assign percentage ratios to each category based on historical data. Break down total energy consumption into individual appliances based on predefined ratios. Generate usage patterns for each appliance category reflecting behavior and ratios.

Additionally, Simulate Nightly Energy Usage in the building to understand how energy is consumed during non-working hours. Use this simulation to assess the effectiveness of the current ESS and explore optimization strategies. By analyzing the simulated nightly energy consumption patterns, identify the optimal ESS capacity that minimizes energy waste and maximizes energy efficiency, ensuring the building meets its Nearly ZEB Level 1 targets.

3. Results

3.3. Comparative Simulation of Enhanced Solar PV Systems

The simulation was conducted across a range of solar PV capacities (180kW, 200kW, 250kW) and ESS storage capacities (0kWh to 500kWh, in increments of 25kWh) as demonstrated in Figure 9. The primary metrics analyzed included energy efficiency, unutilized solar energy, and alignment with the Nearly Zero-Energy Building (ZEB) performance zones defined by the SHASE Japan.

Trends Observed Across Different Solar PV Capacities

• General Observation: Across all configurations, as ESS storage capacity increases, the total energy usage reduction also increases, while the percentage of unutilized solar energy decreases. The movement from ZEB Oriented Building (35%-50%) to Nearly ZEB Level 1 (87.5%-100%) zones indicate progressive improvements in energy efficiency as both solar PV and ESS capacities increase.
• 160kW Solar PV System: Increasing the ESS capacity beyond 50kWh could potentially reduce the percentage of unutilized solar energy, pushing the building’s performance closer to the Nearly ZEB Level 1 zone but the current setup does not yet reach the Nearly ZEB Level 1 zone (87.5%-100%).
• 180kW Solar PV System: The energy usage reduction shows improvement compared to the 160kW system, advancing further into the ZEB Ready zone. However, it still falls short of achieving the Nearly ZEB Level 1 zone (87.5%-100%), indicating that while performance has improved, additional enhancements in either ESS capacity or PV system size are required to reach the highest energy efficiency levels. Unutilized solar energy decreases faster as ESS capacity increases, indicating better alignment between generation and storage.
• 200kW Solar PV System: The total energy reduction begins to reach the Nearly ZEB Level 1 zone at ESS capacities around 275kWh. There is a consistent decrease in unutilized solar energy, making the system significantly more efficient at utilizing the generated solar energy. This configuration represents a more balanced approach, improving both energy efficiency and solar energy utilization compared to lower-capacity systems.
• 250kW Solar PV System: The 250kW system is the most efficient in terms of energy usage reduction, achieving Nearly ZEB Level 1 at ESS capacities starting from 175 kWh. Unutilized solar energy is minimized more effectively, even at lower ESS capacities, making this configuration potentially the most balanced and optimal for maximizing energy efficiency and solar energy utilization.

Key Simulation Insights for Optimization

Figure 10. This figure demonstrates that the 200 kW system reaches the Nearly ZEB Level 1 zone with an ESS capacity of approximately 275 kWh, while the 250 kW system achieves Nearly ZEB Level 1 with an ESS capacity starting from 175 kWh.

Figure 10. This figure demonstrates that the 200 kW system reaches the Nearly ZEB Level 1 zone with an ESS capacity of approximately 275 kWh, while the 250 kW system achieves Nearly ZEB Level 1 with an ESS capacity starting from 175 kWh.

In both cases, unutilized solar energy is effectively minimized, particularly in the 250 kW configuration, which optimally balances solar generation with storage capabilities. These findings suggest that strategically increasing both solar PV capacity and ESS storage is essential for advancing the building towards the Nearly ZEB Level 1 zone, where energy losses are minimized, and energy self-sufficiency is maximized.

This approach should be a primary focus for building managers and energy planners who aim to achieve the highest levels of energy efficiency and sustainability in Zero-Energy Buildings.

Analyze Behavioral Energy Consumption Patterns

The analysis of total energy consumption involves breaking down the data into contributions from individual appliances, such as HVAC systems, lighting, and electronics, based on predefined percentage ratios derived from historical data. This method allows us to transform the daily energy consumption data into patterns that accurately reflect the energy usage of different appliance groups, enabling more targeted energy management strategies.

Table 1. Group of Appliance Behavior and Ratios on Working day Office Building.

Table 1. Group of Appliance Behavior and Ratios on Working day Office Building.

The table above summarizes the behavior and energy consumption ratios of different appliance groups within the office building during a typical working day. The data were collected based on specific time periods, categorizing usage into daytime working hours and nighttime non-working hours. The HVAC system, for example, shows a gradual increase in consumption starting at 08:00 AM, peaking between 13:00 and 16:00 with a 40% increase in usage. Indoor lighting maintains steady usage during working hours, while outdoor lighting and nighttime indoor lighting have minimal usage. Electronics and other devices exhibit steady consumption during the day with a slight reduction at night. This detailed breakdown is crucial for understanding how different appliances contribute to overall energy consumption and for optimizing energy management strategies.

The graph illustrated in Figure 11 highlights the energy consumption patterns throughout a typical working day, with noticeable increases during the late morning and early afternoon, peaking around midday. It indicates that energy usage is lowest during the early morning hours and after working hours in the evening, consistent with reduced activity in the building during those times. This detailed daily pattern is crucial for understanding the building’s energy needs and for optimizing energy management strategies as disaggregation shown in Figure 12.

Simulate Appliance Patterns form Behavioral Data and Appliance Ratios

Switch to Nightly Building Consumption Mode Based on Appliance Ratios and Behavioral Data

Nightly building consumption patterns using switch to night mode scenario based on appliance ratios, behavior, and total consumption data, with an assumed total daily energy consumption of 786 kWh. This simulation is conducted with the same 160 kW solar system as the basis. By applying the predefined appliance ratios and considering typical building behaviors during nighttime hours, the simulation models the contribution of different appliances to the overall energy usage at night. This analysis helps to understand how energy is distributed among various appliances and identifies opportunities for optimizing energy management, particularly in reducing consumption during low-activity periods. The findings can be used to refine energy strategies, such as adjusting appliance operation schedules or enhancing the efficiency of the solar system and energy storage usage during the night.

Figure 14. This figure illustrates the energy usage reduction for a building primarily consuming energy during night hours, simulated with a 160kW solar system and varying ESS (Energy Storage System) capacities.

Figure 14. This figure illustrates the energy usage reduction for a building primarily consuming energy during night hours, simulated with a 160kW solar system and varying ESS (Energy Storage System) capacities.

The goal is to identify the point at which the building’s energy efficiency reaches the Nearly ZEB (Zero Energy Building) Level 1 zone, characterized by an energy reduction of 87.5% to 100%. As shown in the graph, the energy reduction percentage increases as the ESS capacity is expanded. The transition into the Nearly ZEB Level 1 zone occurs at an ESS capacity of 475 kWh. At this point, the energy efficiency of the building has been optimized to a level where nearly all of the solar energy generated is utilized effectively, minimizing energy losses and aligning with the Nearly ZEB Level 1 criteria. This analysis indicates that for a building with substantial night-time energy consumption, a 160kW solar system paired with a 475kWh ESS is necessary to achieve the highest energy efficiency standards.

4. Conclusions

Figure 15. The contour plot presented in this research illustrates that achieving the Nearly ZEB Level 1 zone (87.5%-100% energy reduction) is feasible with the right combination of solar PV capacity and ESS storage.

Figure 15. The contour plot presented in this research illustrates that achieving the Nearly ZEB Level 1 zone (87.5%-100% energy reduction) is feasible with the right combination of solar PV capacity and ESS storage.

5. Discussion

Abbreviations

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