Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Arturs Nikulins, Kaspars Sudars (), Edgars Edelmers, Ivars Namatevs, Kaspars Ozols, Vitalijs Komasilovs, Aleksejs Zacepins, Armands Kviesis and Andreas Reinhardt
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Arturs Nikulins: Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
Kaspars Sudars: Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
Edgars Edelmers: Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
Ivars Namatevs: Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
Kaspars Ozols: Institute of Electronics and Computer Science, LV-1006 Riga, Latvia
Vitalijs Komasilovs: Institute of Computer Systems and Data Science, Faculty of Engineering and Information Technologies, Latvia University of Life Sciences and Technologies, LV-3001 Jelgava, Latvia
Aleksejs Zacepins: Institute of Computer Systems and Data Science, Faculty of Engineering and Information Technologies, Latvia University of Life Sciences and Technologies, LV-3001 Jelgava, Latvia
Armands Kviesis: Institute of Computer Systems and Data Science, Faculty of Engineering and Information Technologies, Latvia University of Life Sciences and Technologies, LV-3001 Jelgava, Latvia
Andreas Reinhardt: Department of Informatics, Technische Universität Clausthal, 38678 Clausthal-Zellerfeld, Germany

Energies, 2024, vol. 17, issue 5, 1-12

Abstract: This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model’s predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server.

Keywords: sustainable energy systems; renewable energy sources; hydrogen production; deep learning; weather forecasting; fully connected neural networks; convolutional neural networks; energy management; wind power; solar power (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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