Solar radiation prediction: A multi-model machine learning and deep learning approach

The increasing integration of renewable energies into electrical grids necessitates accurate forecasting of meteorological variables, particularly solar irradiance. This study presents a novel long-term solar irradiance forecasting approach, utilizing meteorological data from the National Renewable Energy Laboratory spanning 1988–2022. Focusing on five input variables—solar irradiance, dew point, temperature, relative humidity, and wind speed—this study evaluates the predictive performance of 13 data-driven models, comprising ten machine learning (ML) and three deep learning (DL) algorithms. Among them, gradient boosting regressor (GBR) and recurrent neural network (RNN) emerged as top performers in ML and deep learning, respectively. In order to choose the most suitable model for the long and short term, four forecast time-horizons (1, 8, 16, and 24 h) were also taken into consideration for the accurate models. A feature selection process using Pearson’s coefficient identified the most relevant inputs, while quantile regression was employed for uncertainty assessment, mean prediction interval, and prediction interval coverage probability models. This study demonstrates that RNN excels in short-term predictions, while GBR is more effective for long-term forecasts. A new hybrid approach GBR-RNN model was developed, achieving superior performance in terms of RMSE, MAE, and R2 metrics. This multi-model approach, integrating both ML and DL techniques, enhances solar irradiance forecasting by addressing input uncertainty and considering various forecast horizons. The findings contribute to the ongoing advancement of renewable energy forecasting by providing robust, accurate, and uncertainty-aware predictive models. Moreover, this approach helps identify the best-performing model, enabling more reliable and precise solar irradiance forecasts for energy management. This highlights both the improvement in forecasting methods and the importance of selecting the best model for accuracy.