Machine Learning-Based Prediction of Solar Energy Generation Using Weather and Environmental Data

Abstract

Although solar is now playing a growing role in the move to low carbon electricity systems, PV electricity is highly variable, posing a constant challenge in energy planning, grid integration, energy storage and reliable electricity supply. The solar output is greatly affected by the variations in the atmospheric and environmental conditions like solar irradiance, temperature, humidity, cloud cover, wind speed, precipitation, seasonality and others which are location dependent. Therefore, it is important to understand the interaction between these variables and the generation from solar sources to enable more context-specific forecasting to be developed. There has been a recent surge of interest in machine learning (ML), given its data-driven, nonlinear, and time-dependent architectures, able to uncover complex relationships in meteorological and historical energy information (Alcañiz et al. 2023; Gaboitaolelwe et al. 2023). At the same time, the advancements of ML-based solar forecasting have raised concerns on the quality of the data, contextual dependence, complexity, interpretability and transferability of the models to different climatic and geographical conditions.This research study explores the current literature on the application of ML techniques to forecast solar-energy production based on weather and environmental information. The study is designed in an interpretivist, exploratory and qualitative research genre, using a documentary research approach that integrates peer-reviewed scholarly literature, technical publications and authoritative evidence related to the solar forecasting with ML. The study does not involve a new computational experiment, but rather uses a qualitative document analysis and thematic analysis to uncover patterns, conceptual relationships, methodological issues, and practical implications in the current research literature. The synthesis identifies five related observations. Weather and environmental variables are fundamental predictors since they directly affect solar resource availability and PV output, with variations in atmospheric conditions. Second, ML methods, such as traditional ML, ANNs, and deep-learning methods, have significant potential to identify nonlinear and complex patterns that are hard to capture using conventional methods (Alcañiz et al., 2023; Tian et al., 2023). Third, the usefulness of the predictions is critically dependent on the quality of the data, the temporal resolution, the geographical context, climate characteristics and environmental variability. Fourth, the greater the complexity of the model, the more important it becomes to be interpretable and explainable, especially when their forecasts feed operational and energy-management decisions. Recent studies also highlight the need for data-driven learning to enhance contextual robustness and generalization (de Oliveira Santos et al., 2024). Last but not least, it is important to recognize that ML-based forecasting is not a standalone solution, but a decision support tool that can be integrated into renewable-energy planning.The study finds that, to be effective, solar-energy prediction must be more than just choosing complex algorithms. It needs variables that are environmentally relevant, reliable and context sensitive data, clear analytical processes and careful consideration of climatic and geographical variables. Therefore, future research should focus on approaches that are explainable, hybrid, physics-informed, and uncertainty-aware and can provide reliable and responsible management of renewable energies.