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Deep Learning Neural Networks for Short-Term PV Power Forecasting via Sky Image Method

Author

Listed:
  • Wen-Chi Kuo

    (Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

  • Chiun-Hsun Chen

    (Department of Aerospace and Systems Engineering, Feng Chia University, Taichung 407, Taiwan)

  • Sih-Yu Chen

    (Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

  • Chi-Chuan Wang

    (Department of Mechanical Engineering, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan)

Abstract

Solar photovoltaic (PV) power generation is prone to drastic changes due to cloud cover. The power is easily affected within a very short period of time. Thus, the accuracy of grasping cloud distribution is important for PV power forecasting. This study proposes a novel sky image method to obtain the cloud coverage rate used for short-term PV power forecasting. The authors developed an image analysis algorithm from the sky images obtained by an on-site whole sky imager (WSI). To verify the effectiveness of cloud coverage rate as the parameter for PV power forecast, four different combinations of weather features were used to compare the accuracy of short-term PV power forecasting. In addition to the artificial neural network (ANN) model, long short-term memory (LSTM) and the gated recurrent unit (GRU) were also introduced to compare their applicability conditions. After a comprehensive analysis, the coverage rate is the key weather feature, which can improve the accuracy by about 2% compared to the case without coverage feature. It also indicates that the LSTM and GRU models revealed better forecast results under different weather conditions, meaning that the cloud coverage rate proposed in this study has a significant benefit for short-term PV power forecasting.

Suggested Citation

  • Wen-Chi Kuo & Chiun-Hsun Chen & Sih-Yu Chen & Chi-Chuan Wang, 2022. "Deep Learning Neural Networks for Short-Term PV Power Forecasting via Sky Image Method," Energies, MDPI, vol. 15(13), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4779-:d:851451
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    References listed on IDEAS

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    1. Kong, Weicong & Jia, Youwei & Dong, Zhao Yang & Meng, Ke & Chai, Songjian, 2020. "Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting," Applied Energy, Elsevier, vol. 280(C).
    2. Kamadinata, Jane Oktavia & Ken, Tan Lit & Suwa, Tohru, 2019. "Sky image-based solar irradiance prediction methodologies using artificial neural networks," Renewable Energy, Elsevier, vol. 134(C), pages 837-845.
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    Cited by:

    1. Mohamed Trabelsi & Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Shady S. Refaat & Tingwen Huang & Fakhreddine S. Oueslati, 2022. "An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting," Energies, MDPI, vol. 15(23), pages 1-14, November.
    2. Nunes Maciel, Joylan & Javier Gimenez Ledesma, Jorge & Hideo Ando Junior, Oswaldo, 2024. "Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    3. A-Hyun Jung & Dong-Hyun Lee & Jin-Young Kim & Chang Ki Kim & Hyun-Goo Kim & Yung-Seop Lee, 2022. "Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea," Energies, MDPI, vol. 15(21), pages 1-13, October.

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