SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting

Abstract

Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (LSTM-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The LSTM-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution, different input features, different optimizations of the same models, or other user preferences.