Machine Learning Prediction of AC Power Output in Solar Cells

Abstract

Solar power is used to combat climate change and promote sustainable development. However, the efficiency and reliability of solar energy systems are heavily reliant on accurately forecasting solar output. We aimed to identify the most effective machine learning algorithm from the scikit-learn library for predicting the AC power output of solar cells. Unlike previous models that failed to capture changes in environmental conditions or complex dependencies influencing solar cell performance, we found a model that integrates various input parameters, including daily yield, total yield, ambient temperature, module temperature, irradiation, and month. Our goal was to find an algorithm that would enable accurate forecasts across different seasons and improve long-term prediction capabilities. This robustness is crucial for real-world applications, helping stakeholders in the energy sector make informed decisions, enhance grid reliability, promote renewable energy integration, and expedite the shift toward sustainable energy. Through testing various algorithms, the Random Forest Regressor model demonstrated highest accuracy with an R-Squared score of 0.968. This indicates the model’s proficiency in identifying key factors affecting solar energy generation and predicting future solar output with minimal error across a range of renewable energy applications. Grid operators can use the model’s predictions to optimize power distribution, while solar companies can enhance module and tracker placement for better efficiency. With the potential to improve financial returns for solar investors and strengthen the bankability for finance partners, this model emerges as a valuable tool for developers, vendors, and energy offtake partners in maximizing the potential of renewable energy installations.