Investigation of Machine Learning for Predicting the Output of Photovoltaic Solar Power

The production of solar energy is becoming a more significant part of the world's energy structure, yet its fluctuation poses special problems for grid integration and energy management. For the purpose of providing a dependable and effective power supply, accurate forecasting of solar power output is essential. This article offers a thorough method for predicting solar power output using machine learning techniques. To create forecast models, our methodology combines historical meteorological data with information on solar power generation. To capture the intricate relationships in the data, we use a variety of machine learning algorithms, such as neural networks, support vector machines, and fuzzy logic. To improve model performance, feature engineering and data preprocessing methods are used. The results of this study have important ramifications for the renewable energy sector, grid operators, and decisionmakers, providing a strategy to maximize solar energy and lessen reliance on fossil fuels.