Stacked Ensemble–Neural Network Hybrid Framework for Autonomous Control and Optimization of Solar Photovoltaic Energy Systems

Abstract

The growing demand for renewable energy around the world, many countries are adding solar power to their energy programs. Solar photovoltaic (PV) systems can affect the stability and quality of the electricity grid since solar radiation can come and go, especially in big installations. Solar fluctuations can lead to either excessive or insufficient power generation, therefore accurate forecasting is essential for effective energy management and system integration. A major area of study is autonomous control and optimisation of solar photovoltaic energy systems. This study presents data preparation and transformation methodologies aimed at enhancing data quality and model efficacy in forecasting. Kernel Density Estimation (KDE) and Pearson Correlation Coefficient (PCC) feature selection help find important characteristics and cut down on prediction mistakes. LSTM and XGBoost are the basic models that DES-XG, a frequently used stacked ensemble method, employs. Extreme gradient boosting combines the outputs of basic learners to create final predictions. Tests reveal that the proposed DES-XG model works better than both the LSTM and XGBoost models on their own, with an accuracy of 95.42% and better stability and consistency across case studies.