Improved Regression Models for Solar Photovoltaic Power Output Prediction

The rising demand for renewable energy sources underscores the importance of efficient solar photovoltaic (PV) power plant operation. Accurate power output prediction plays a key role in optimizing energy management and ensuring the reliability of power systems. This research focuses on comparing the performance of regression models for predicting power output, in PV power plants. The models examined include Linear Regression, Poisson Regression, Lasso Regression, Stochastic Gradient Descent (SGD) regression, and Decision Tree Regression. The study utilizes a comprehensive dataset based on real world parameters for evaluating the efficacy of each regression model. Preprocessing steps, such as data cleaning and feature scaling, are implemented to ensure the robustness of the models. The data is partitioned into training sets and testing sets, and various performance metrics, including Mean Squared Error (MSE), R-squared (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) are employed to estimate the accuracy and generalization capabilities of these models. The results obtained reveal nuanced insights into the highlights and challenges of each regression model, providing insights into their suitability in predicting power output in real-world PV power plant scenarios. The findings of this study contribute to the expanding field of research on solar energy prediction and offer valuable guidance for practitioners and researchers seeking optimal regression models for PV power plant power prediction