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The importance of renewable energy sources has been increasing in recent years due to the advantages they provide, in terms of the preservation of the environment and inexhaustible supply of energy. In particular, photovoltaic (PV) energy will be prioritized in the future and it is expanding worldwide. However, the high variability of solar irradiance and, consequently, of PV power generation causes uncertainty in the planning and operation of power systems. Thus, PV power generation forecasting is crucial to solve this issue. This paper presents the comparison of LSTM-based methods for the 6-hour-ahead and day-ahead forecasting of PV power generated by a PV power station, based on the Global Horizontal Irradiance and the air temperature forecasts of the same period.

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Comparison of LSTM-Based Models for the 6-Hour-Ahead and Day-Ahead Forecasting of Photovoltaic Power Generation

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  • 发表日期：

    2025年

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  • Giorgia Ghione,
  • Vincenzo Randazzo,
  • Eros Pasero,
  • Marco Badami

摘要

The importance of renewable energy sources has been increasing in recent years due to the advantages they provide, in terms of the preservation of the environment and inexhaustible supply of energy. In particular, photovoltaic (PV) energy will be prioritized in the future and it is expanding worldwide. However, the high variability of solar irradiance and, consequently, of PV power generation causes uncertainty in the planning and operation of power systems. Thus, PV power generation forecasting is crucial to solve this issue. This paper presents the comparison of LSTM-based methods for the 6-hour-ahead and day-ahead forecasting of PV power generated by a PV power station, based on the Global Horizontal Irradiance and the air temperature forecasts of the same period.