Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems

Abstract

The integration of photovoltaic (PV) systems into the global energy landscape has been boosted in recent years, driven by environmental concerns and research into renewable energy sources. The accurate prediction of temperature and solar irradiance is essential for optimizing the performance and grid integration of PV systems. Machine learning (ML) has become an effective tool for improving the accuracy of these predictions. This comprehensive review explores the pioneer techniques and methodologies employed in the field of ML-based forecasting of temperature and solar irradiance for PV systems. This article presents a comparative study between various algorithms and techniques commonly used for temperature and solar radiation forecasting. These include regression models such as decision trees, random forest, XGBoost, and support vector machines (SVM). The beginning of this article highlights the importance of accurate weather forecasts for the operation of PV systems and the challenges associated with traditional meteorological models. Next, fundamental concepts of machine learning are explored, highlighting the benefits of improved accuracy in estimating the PV power generation for grid integration.

Artificial intelligence applications in solar energy

“…The scientific discussion on several ML-based approaches in solar prediction highlights the wide variety of obtainable methodologies and the distinctive contributions each of these methodologies makes to enhancing the precision and dependability of solar radiation forecasting procedures. These studies provide valuable insights into the strengths and limits of different machine learning algorithms, therefore paving the path for improved usage of solar energy and grid integration [319]- [321].…”