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

Ghadah Alkhayat, Syed Hamid Hasan and Rashid Mehmood ()
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Ghadah Alkhayat: Department of Computer Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Syed Hamid Hasan: Department of Computer Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Rashid Mehmood: High Performance Computing Centre, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Energies, 2022, vol. 15, issue 18, 1-55

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.

Keywords: solar energy forecasting; generalizability; long short-term memory (LSTM); gated recurrent unit (GRU); convolutional neural network (CNN); hybrid CNN-bidirectional LSTM; LSTM autoencoder (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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