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Multi-step solar irradiation prediction based on weather forecast and generative deep learning model

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  • Gao, Yuan
  • Miyata, Shohei
  • Akashi, Yasunori

Abstract

With the rapid development of computer technology, more and more deep learning models are used in solar radiation (irradiation) prediction. There have been a lot of studies discussing the research of this type of model. However, how to better apply the deep learning model in the optimization method of building energy system, such as multi-step solar radiation (irradiation) prediction model in model predictive control (MPC), is still a challenging issue due to the complexity of the time series and the accumulation of errors in multi-step forecasts.

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  • Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-step solar irradiation prediction based on weather forecast and generative deep learning model," Renewable Energy, Elsevier, vol. 188(C), pages 637-650.
    • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:637-650
    DOI: 10.1016/j.renene.2022.02.051
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    References listed on IDEAS

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    Cited by:

    1. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).

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