Poland’s photovoltaic (PV) sector has been witnessing a consistent upswing, with various factors contributing to the advancement of photovoltaic technology in the nation. Similarly to many other European Union (EU) member states, Poland has established ambitious renewable energy targets to bolster the proportion of renewables in its energy mix. Consequently, this impetus has triggered substantial investments in solar energy and PV technology. The government has proactively introduced diverse support mechanisms, such as feed-in tariffs, auctions, and subsidies, to foster the growth of renewable energy initiatives, including solar PV installations. Being an EU member state, Poland enjoys access to funding programs that facilitate the transition to renewable energy sources, which has further accelerated the development of photovoltaic projects in the country. Encouragingly, the cost of PV technology has been consistently declining over the years, rendering solar energy increasingly economically viable and appealing to both investors and consumers alike. Simultaneously, there has been a concerted effort to reduce dependence on imported fossil fuels, leading to a notable emphasis on fostering domestic renewable energy sources, including solar power. Notably, investments in PV technology have enabled Poland to diversify its energy sources, thereby significantly bolstering energy security. A remarkable milestone occurred between 2019 and 2022, as over one million new PV micro-installations were established, primarily focusing on single-family households. These PV micro-installations are thoughtfully tailored to align with specific energy consumption patterns, spanning distinct periods, such as a year, adhering dutifully to prosumer rules within Poland’s regulatory framework.

The principal aim of this study centers around evaluating the feasibility and precision of employing PV energy production calculation methods under Polish conditions. Researchers have observed that presently utilized methods for calculating energy production may exhibit inaccuracies, stemming from inadequate adaptation to terrain, location, wind conditions, and the representative characteristics of PV panels within the installation. In this research, the application of Deep Neural Network (DNN) methods will be explored to verify the accuracy of data provided by SolarEdge, a widely renowned simulator. Emphasis will be placed on hybrid Long Short-Term Memory (LSTM) based architectures to carry out this assessment effectively.

Furthermore, the statistical analysis conducted in this study meticulously evaluates representative characteristics of the one-year analysis period. This comprehensive investigation marks the first-ever assessment of energy production from a micro-inverter installation under simulated conditions within Central Eastern Europe, specifically in the Swietokrzyskie Voivodeship region.

Simulation-based methods and AI predictions represent distinct approaches applied in the estimation of electricity generation from renewable energy sources (RES). Let’s delve into each of these methodologies:

Below we present our comparison between values reported by simulations and our DNN models.

Based on the DNN (Deep Neural Network) metrics provided, here is a description of the MultiLSTM results:

Overall, the DNN seems to be performing reasonably well, with relatively low errors and high explained variance and R-squared values.

In this scenario, the analysis reveals the following performance metrics for the model:

EncoderDecoderLSTM model demonstrates reasonable performance within the context and specific domain of the problem.

Emphasizing the significance of data abundance for model improvement, it becomes evident that increasing the data-set enhances output quality, particularly after applying feature selection. The accuracy of models has been assessed using R2 and NMAE metrics, with the data split into 80% for training and 20% for testing.

Choosing the best model manually can be challenging, relying solely on metrics and visual representations. To address this, an automated approach has been adopted. Leveraging benchmarks and a framework, proper hyperparameters and optimized models have been selected, considering factors like calculation costs, weight, and performance.

The automated environment presents a distinct advantage in enabling easy comparison of results. Metrics are measured for each method and learning stage, unveiling the advantages and drawbacks of different architectures. Focus on optimization aspects becomes achievable, especially when evaluating multiple horizons and methodologies. It is worth noting that the evaluation process took approximately one and a half months.

To delve into the intricacies of energy forecasting, the models’ behavior has been extensively tested concerning energy production from individual photovoltaic microinstallation based on example from Swietokrzyskie Voivodeship Poland . This examination is based on the total energy output from micro-installation.

In conclusion, this pioneering study seeks to address the challenges posed by existing PV energy production calculation methods in Polish conditions. By identifying potential inaccuracies due to factors such as terrain, location, wind conditions, and PV panel representative characteristics, the research highlights the importance of refining these methods for more precise results.

To achieve this, the study explores the application of Deep Neural Network (DNN) methods, with a particular focus on hybrid Long Short-Term Memory (LSTM) based architectures. Through the evaluation of data provided by the esteemed SolarEdge simulator, the accuracy of energy production calculations can be thoroughly verified.

Moreover, this research stands as a significant milestone as it pioneers the assessment of energy production from a microinverter installation under simulated conditions in the Central Eastern European region, specifically in the Swietokrzyskie Voivodeship area. The statistical analysis conducted in this study meticulously examines the representative characterstics of the one-year analysis period, providing valuable insights into the potential of PV energy generation in this specific context.

Overall, this study’s findings and methodologies pave the way for future advancements in PV energy production calculations, fostering the development of more accurate and efficient methods tailored to the unique conditions of Poland and similar regions. With the integration of cutting-edge DNN techniques, the renewable energy industry can take a step closer to realizing its full potential in achieving a sustainable and eco-friendly future.