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Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery

Author

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  • Myeongchan Oh

    (New & Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Chang Ki Kim

    (New & Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Boyoung Kim

    (New & Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Changyeol Yun

    (New & Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Yong-Heack Kang

    (New & Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

  • Hyun-Goo Kim

    (New & Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research, Daejeon 34129, Korea)

Abstract

Solar forecasting is essential for optimizing the integration of solar photovoltaic energy into a power grid. This study presents solar forecasting models based on satellite imagery. The cloud motion vector (CMV) model is the most popular satellite-image-based solar forecasting model. However, it assumes constant cloud states, and its accuracy is, thus, influenced by changes in local weather characteristics. To overcome this limitation, satellite images are used to provide spatial data for a new spatiotemporal optimized model for solar forecasting. Four satellite-image-based solar forecasting models (a persistence model, CMV, and two proposed models that use clear-sky index change) are evaluated. The error distributions of the models and their spatial characteristics over the test area are analyzed. All models exhibited different performances according to the forecast horizon and location. Spatiotemporal optimization of the best model is then conducted using best-model maps, and our results show that the skill score of the optimized model is 21% better than the previous CMV model. It is, thus, considered to be appropriate for use in short-term forecasting over large areas. The results of this study are expected to promote the use of spatial data in solar forecasting models, which could improve their accuracy and provide various insights for the planning and operation of photovoltaic plants.

Suggested Citation

  • Myeongchan Oh & Chang Ki Kim & Boyoung Kim & Changyeol Yun & Yong-Heack Kang & Hyun-Goo Kim, 2021. "Spatiotemporal Optimization for Short-Term Solar Forecasting Based on Satellite Imagery," Energies, MDPI, vol. 14(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2216-:d:537097
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    References listed on IDEAS

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    1. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    2. Kosmopoulos, Panagiotis & Dhake, Harshal & Melita, Nefeli & Tagarakis, Konstantinos & Georgakis, Aggelos & Stefas, Avgoustinos & Vaggelis, Orestis & Korre, Valentina & Kashyap, Yashwant, 2024. "Multi-Layer Cloud Motion Vector Forecasting for Solar Energy Applications," Applied Energy, Elsevier, vol. 353(PB).
    3. Chu, Yinghao & Wang, Yiling & Yang, Dazhi & Chen, Shanlin & Li, Mengying, 2024. "A review of distributed solar forecasting with remote sensing and deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 198(C).
    4. Xie, Qiyue & Ma, Lin & Liu, Yao & Fu, Qiang & Shen, Zhongli & Wang, Xiaoli, 2023. "An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extraction," Renewable Energy, Elsevier, vol. 219(P2).

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