Machine Learning for Solar Power Prediction: A Data-Driven Framework for Renewable Energy Forecasting

Abstract

Forecasting solar power has become a major issue to grid integration and effective management of energy in renewable energy systems. Solar irradiance is intermittent and nonlinear and therefore needs sophisticated predictive models that are not limited to conventional statistical models. In this paper, the authors use a machine learning and deep learning-based approach to derive a complex data-driven solar power prediction framework. Our research applies a combination of several advanced methods such as feature engineering, temporal decomposition and hybrid ensemble algorithms to have both the short-term variability and long-term trends of solar generation. We build and test various prediction models such as LSTM, XGBoost, Random Forest and hybrid CNN-LSTM using a com-bination of meteorological information, past power production and other sophisticated preprocessing methods. Evidence gained through experimentation on actual photovoltaic real-world data indicates that our hybrid structure can do better with mean absolute error improvements of up to 44.55 percent and R-square values of over 0.98. The modular structure of the framework allows the scalability of the structure to the various geographic regions and solar technologies, offering viable information to grid operators and energy planners. This study will help to enhance the integration of renewable energy and aid the process of changing to the sustainable power systems.