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Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

Author

Listed:
  • Filipe D. Campos

    (Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal)

  • Tiago C. Sousa

    (Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal)

  • Ramiro S. Barbosa

    (Department of Electrical Engineering, Institute of Engineering—Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal
    GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, ISEP/IPP, 4249-015 Porto, Portugal)

Abstract

In recent times, renewable energy sources have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of artificial intelligence (AI), as it presents a solution for predicting photovoltaic energy production. The basis of the AI models is provided from two data sets, one for generated electrical power and another for meteorological data, related to the year 2017, which are freely available on the Energias de Portugal (EDP) Open Project website. The implemented AI models rely on long short-term memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60-min horizon based on meteorological variables. The performance of the models is evaluated using the performance indicators MAE, RMSE, and R 2 , for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.

Suggested Citation

  • Filipe D. Campos & Tiago C. Sousa & Ramiro S. Barbosa, 2024. "Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM," Energies, MDPI, vol. 17(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:11:p:2582-:d:1402892
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    References listed on IDEAS

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    1. Seung-Min Jung & Sungwoo Park & Seung-Won Jung & Eenjun Hwang, 2020. "Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities," Sustainability, MDPI, vol. 12(16), pages 1-20, August.
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    3. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang, 2022. "Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method," Energies, MDPI, vol. 15(11), pages 1-21, June.
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