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Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production

Author

Listed:
  • 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)

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.

Suggested Citation

  • Arturs Nikulins & Kaspars Sudars & Edgars Edelmers & Ivars Namatevs & Kaspars Ozols & Vitalijs Komasilovs & Aleksejs Zacepins & Armands Kviesis & Andreas Reinhardt, 2024. "Deep Learning for Wind and Solar Energy Forecasting in Hydrogen Production," Energies, MDPI, vol. 17(5), pages 1-12, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:5:p:1053-:d:1344387
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    References listed on IDEAS

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    1. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    2. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    3. Ghadah Alkhayat & Syed Hamid Hasan & Rashid Mehmood, 2022. "SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting," Energies, MDPI, vol. 15(18), pages 1-55, September.
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    Cited by:

    1. Bozzolo Lueckel, Fabio B. & Monaghan, Rory F.D. & Lynch, Muireann Á., 2025. "Hydrogen supply chain modelling at energy system scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    2. Ling Miao & Ning Zhou & Jianwei Ma & Hao Liu & Jian Zhao & Xiaozhao Wei & Jingyuan Yin, 2025. "Current Status, Challenges and Future Perspectives of Operation Optimization, Power Prediction and Virtual Synchronous Generator of Microgrids: A Comprehensive Review," Energies, MDPI, vol. 18(13), pages 1-41, July.
    3. Baseer, Mohammad Abdul & Kumar, Prashant & Nascimento, Erick Giovani Sperandio, 2025. "Advancements in hydrogen production through the integration of renewable energy sources with AI techniques: A comprehensive literature review," Applied Energy, Elsevier, vol. 383(C).
    4. Dariusz Bradło & Witold Żukowski & Jan Porzuczek & Małgorzata Olek & Gabriela Berkowicz-Płatek, 2025. "Towards Net Zero in Poland: A Novel Approach to Power Grid Balance with Centralized Hydrogen Production Units," Energies, MDPI, vol. 18(7), pages 1-24, March.

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