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Recent Trends in Real-Time Photovoltaic Prediction Systems

Author

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  • Isaac Gallardo

    (ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain
    RBZ Robot Design S.L., C. Casas de Miravete 24A, 28031 Madrid, Spain)

  • Daniel Amor

    (RBZ Robot Design S.L., C. Casas de Miravete 24A, 28031 Madrid, Spain)

  • Álvaro Gutiérrez

    (ETSI Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain)

Abstract

Photovoltaic power forecasting is an important problem for renewable energy integration in the grid. The purpose of this review is to analyze current methods to predict photovoltaic power or solar irradiance, with the aim of summarizing them, identifying gaps and trends, and providing an overview of what has been achieved in recent years. A search on Web of Science was performed, obtaining 60 articles published from 2020 onwards. These articles were analyzed, gathering information about the forecasting methods used, the horizon, time step, and parameters. The most used forecasting methods are machine learning and deep learning based, especially artificial neural networks. Most of the articles make predictions for one hour or less ahead and predict power instead of irradiance, although both parameters are strongly correlated, and output power depends on received irradiance. Finally, they use weather variables as inputs, consisting mainly of irradiance, temperature, wind speed and humidity. Overall, there is a lack of hardware implementations for real-time predictions, being an important line of development in future decades with the use of embedded prediction systems at the photovoltaic installations.

Suggested Citation

  • Isaac Gallardo & Daniel Amor & Álvaro Gutiérrez, 2023. "Recent Trends in Real-Time Photovoltaic Prediction Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5693-:d:1206199
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    References listed on IDEAS

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    1. Jinhwa Jeong & Dongkyu Lee & Young Tae Chae, 2023. "A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service," Energies, MDPI, vol. 16(22), pages 1-21, November.

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