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Photovoltaic Power Forecast Using Deep Learning Techniques with Hyperparameters Based on Bayesian Optimization: A Case Study in the Galapagos Islands

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  • Richard Guanoluisa

    (Grupo de Investigación en Propagación, Control Electrónico y Networking (PROCONET), Departamento de Eléctrica, Electrónica y Telecomunicaciones, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador)

  • Diego Arcos-Aviles

    (Grupo de Investigación en Propagación, Control Electrónico y Networking (PROCONET), Departamento de Eléctrica, Electrónica y Telecomunicaciones, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador)

  • Marco Flores-Calero

    (Grupo de Investigación en Propagación, Control Electrónico y Networking (PROCONET), Departamento de Eléctrica, Electrónica y Telecomunicaciones, Universidad de las Fuerzas Armadas ESPE, Av. General Rumiñahui s/n, Sangolquí 171-5-231B, Ecuador)

  • Wilmar Martinez

    (Department of Electrical Engineering, ESAT, KU Leuven, Agoralaan gebouw B bus 8, 3590 Diepenbeek, Belgium
    EnergyVille-Thor Park 8310, 3600 Genk, Belgium)

  • Francesc Guinjoan

    (Department of Electronics Engineering, Escuela Técnica Superior de Ingenieros de Telecomunicación de Barcelona, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain)

Abstract

Hydropower systems are the basis of electricity power generation in Ecuador. However, some isolated areas in the Amazon and Galapagos Islands are not connected to the National Interconnected System. Therefore, isolated generation systems based on renewable energy sources (RES) emerge as a solution to increase electricity coverage in these areas. An extraordinary case occurs in the Galapagos Islands due to their biodiversity in flora and fauna, where the primary energy source comes from fossil fuels despite their significant amount of solar resources. Therefore, RES use, especially photovoltaic (PV) and wind power, is essential to cover the required load demand without negatively affecting the islands’ biodiversity. In this regard, the design and installation planning of PV systems require perfect knowledge of the amount of energy available at a given location, where power forecasting plays a fundamental role. Therefore, this paper presents the design and comparison of different deep learning techniques: long-short-term memory (LSTM), LSTM Projected, Bidirectional LSTM, Gated Recurrent Units, Convolutional Neural Networks, and hybrid models to forecast photovoltaic power generation in the Galapagos Islands of Ecuador. The proposed approach uses an optimized hyperparameter-based Bayesian optimization algorithm to reduce the forecast error and training time. The results demonstrate the accurate performance of all the methods by achieving a low-error short-term prediction, an excellent correlation of over 99%, and minimizing the training time.

Suggested Citation

  • Richard Guanoluisa & Diego Arcos-Aviles & Marco Flores-Calero & Wilmar Martinez & Francesc Guinjoan, 2023. "Photovoltaic Power Forecast Using Deep Learning Techniques with Hyperparameters Based on Bayesian Optimization: A Case Study in the Galapagos Islands," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:16:p:12151-:d:1213198
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    References listed on IDEAS

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    1. Houssem Ben Aribia & Ali M. El-Rifaie & Mohamed A. Tolba & Abdullah Shaheen & Ghareeb Moustafa & Fahmi Elsayed & Mostafa Elshahed, 2023. "Growth Optimizer for Parameter Identification of Solar Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    2. Vishnu Suresh & Przemyslaw Janik & Jacek Rezmer & Zbigniew Leonowicz, 2020. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm," Energies, MDPI, vol. 13(3), pages 1-15, February.
    3. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
    4. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    5. Mostafa Elshahed & Ali M. El-Rifaie & Mohamed A. Tolba & Ahmed Ginidi & Abdullah Shaheen & Shazly A. Mohamed, 2022. "An Innovative Hunter-Prey-Based Optimization for Electrically Based Single-, Double-, and Triple-Diode Models of Solar Photovoltaic Systems," Mathematics, MDPI, vol. 10(23), pages 1-22, December.
    6. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    7. Rodriguez, Mauricio & Arcos–Aviles, Diego & Martinez, Wilmar, 2023. "Fuzzy logic-based energy management for isolated microgrid using meta-heuristic optimization algorithms," Applied Energy, Elsevier, vol. 335(C).
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