Machine Learning-Based Solar Energy Forecasting Using Environmental and Weather Data for Smart Renewable Energy Systems

Abstract

With the increasing deployment of solar photovoltaic (PV) technologies, accurate solar-energy forecasting for smart and flexible renewable-energy systems has become more crucial. But solar energy generation is subject to significant variations depending upon the environment and atmosphere as well as time and place, and precise forecasting is particularly difficult because of these factors. Uncertainties associated with solar PV generation, due to variability in solar irradiance, cloudiness, temperature, humidity, wind speed and precipitation, as well as seasonal and time-dependent weather variability, can significantly affect PV generation and uncertainty for energy planning and grid operation. In this context, the current research investigates the potential of machine-learning (ML) techniques in the context of solar-energy forecasting and its contribution to smart renewable-energy management, using environmental and weather data. The study uses qualitative research, interpretivist, and exploratory research design using documentary and secondary sources. It critically synthesizes research articles from peer-reviewed journals, critical reviews, studies on renewable energy, meteorological publications, technical documents and authoritative institutional sources. The thematic synthesis reveals five interrelated findings: environmental and weather parameters are essential input factors for understanding solar energy generation variability; ML techniques offer flexible tools to uncover complex relationships in meteorological and energy-related data; data quality, availability, temporal resolution and uncertainty are significant factors affecting the reliability of forecasts; explainability and context-specific model selection are essential to understanding the forecasting process and to support practical decision-making; and the integration of forecasting capabilities with smart renewable energy systems can enhance energy management and operational coordination. The insights suggest possible applications in energy storage scheduling, smart-grid management, grid balancing, distributed generation, and integration with renewable energy. The prospect of using ML for solar forecasting has great promise for enhancing the responsiveness and coordination of renewable energy systems, but its utility will depend on the quality of data used, the uncertainty of the environment, the transparency of models, and how well they can be applied in the context of particular energy systems.