Enhanced Solar Power Prediction Models With Integrating Meteorological Data Toward Sustainable Energy Forecasting

Abstract

Sustainable energy management hinges on precise forecasting of renewable energy sources, with a specific focus on solar power. To enhance resource allocation and grid integration, this study introduces an innovative hybrid approach that integrates meteorological data into prediction models for photovoltaic (PV) power generation. A thorough analysis is performed utilizing the Desert Knowledge Australia Solar Centre (DKASC) Hanwha Solar dataset encompassing PV output power and meteorological variables from sensors. The aim is to develop a distinctive hybrid predictive model framework by integrating feature selection techniques with various regression algorithms. This model, referred to as the PV power generation predictive model (PVPGPM), utilizes meteorological data specific to the DKASC. In this study, various feature selection techniques are implemented, including Pearson correlation (PC), variance inflation factor (VIF), mutual information (MI), step forward selection (SFS), backward elimination (BE), recursive feature elimination (RFE), and embedded method (EM), to identify the most influential factors for PV power prediction. Furthermore, a hybrid predictive model integrating multiple regression algorithms is introduced, including linear regression, ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Elastic Net, Extra Trees Regressor, random forest regressor, gradient boosting (GB) regressor, eXtreme Gradient Boosting (XGBoost) Regressor, and a hybrid model thereof. Extensive experimentation and evaluation showcase the effectiveness of the proposed approach in achieving high prediction accuracy. Results demonstrate that the hybrid model comprising XGBoost Regressor, Extra Trees Regressor, and GB regressor surpasses other regression algorithms, yielding a minimal root mean square error (RMSE) of 0.108735 and the highest R-squared (R²) value of 0.996228. The findings underscore the importance of integrating meteorological insights into renewable energy forecasting for sustainable energy planning and management.