Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

Mostafa A. Rushdi (), Shigeo Yoshida (), Koichi Watanabe, Yuji Ohya and Amr Ismaiel
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Mostafa A. Rushdi: Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
Shigeo Yoshida: Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
Koichi Watanabe: Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
Yuji Ohya: Research Institute for Applied Mechanics (RIAM), Kyushu University, Fukuoka 816-8580, Japan
Amr Ismaiel: Faculty of Engineering and Technology, Future University in Egypt (FUE), New Cairo 11835, Egypt

Energies, 2024, vol. 17, issue 15, 1-23

Abstract: Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R 2 = 0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R 2 = 0.99647 , but is still considered a competitive model. A reduced model was introduced trading off some accuracy ( R 2 = 0.9916 ) for significantly reduced data collection requirements and effort.

Keywords: hybrid renewable energy systems; wind–solar tower; regression; power prediction; deep learning; artificial neural networks; convolutional neural networks (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: 2024
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