Abstract

The prediction of photovoltaic (PV) output is crucial for efficient utilization of solar energy resources. This research paper presents a comparison analysis of various machine learning techniques for PV prediction using weather sensor data to identify the most effective and accurate approach for forecasting PV output based on weather conditions. The research utilizes a dataset consisting of historical weather sensor measurements and corresponding PV output data. Multiple machine learning techniques including such as linear regression, decision trees, random forests, support vector machines, neural networks, and gradient boosting methods are employed and evaluated. The major challenge of solar power is its irrepressible variation since it is extremely depending on other climate variables. Performance estimation metrics such as root mean squared error (RMSE) and mean absolute error (MAE) are used to assess the prediction accuracy of each technique. The experimental results are analyzed and compared, highlighting the strengths and limitations of each method. The findings of this study provide insights into the effectiveness of different machine learning techniques for PV prediction using weather sensor data. The results can aid researchers and practitioners in selecting the most suitable approach for accurate PV output forecasting, contributing to the advancement of renewable energy integration and management systems.