Solar Radiation Prediction using Machine Learning

Solar energy being the most abundant and sustainable kind of energy is thus one of the major contributors to the clean energy transition globally. Predictions of solar radiation must be accurate because they optimize the performance of various solar energy systems, such as photovoltaic panels and solar thermal plants. The study at hand has focused on machine learning for the prediction of solar radiation extensively. Solar radiation data for past instances were modelled and trained under the machine learning models based on some other meteorological parameters, geographical and time-related features. The predictive performances of such models were tested in a real environment in different geographic regions and climates. From such an outcome, it is justified that machine-learning algorithms are handy tools for accurately predicting solar radiation levels. In general, these predictions help energy developers, grid operators, and operators of solar energy systems in deciding the optimum generation, distribution, and consumption of energy. The study also highlights the crucial aspect of feature engineering, model selection, and hyperactive parameter tuning in effective prediction.