Recent Advances and Future Challenges of Solar Power Generation Forecasting

Abstract

The unprecedented growth of Renewable Energy Sources (RES) positions solar power as a leading contender in the global energy mix. Solar energy offers a sustainable alternative to fossil fuels, mitigating carbon emissions and promoting environmental sustainability. This study explores the crucial role of forecasting algorithms within photovoltaic (PV) systems. We aim to provide a comprehensive understanding of methodologies, datasets, and recent advancements for enhancing predictive accuracy in solar power generation forecasting. While machine learning has dominated previous research, recent studies highlight challenges in achieving optimal efficiency and accuracy. A significant obstacle lies in the deficiency of real-world application for large-scale specifically for solar power generation forecasting. To address this gap, this study defines prevalent forecasting methodologies and illuminates datasets with diverse characteristics and their relevance. This study meticulously provides and explore recent advanced methods and datasets, emphasizing their impact on forecasting performance. This study not only deepens our understanding of existing methodologies but also provides valuable insights for future advancements in solar power generation forecasting.

Keywords

• Solar power
• machine learning
• PV
• solar energy