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A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant

Author

Listed:
  • Wang, Fei
  • Lu, Xiaoxing
  • Mei, Shengwei
  • Su, Ying
  • Zhen, Zhao
  • Zou, Zubing
  • Zhang, Xuemin
  • Yin, Rui
  • Duić, Neven
  • Shafie-khah, Miadreza
  • Catalão, João P.S.

Abstract

Accurate ultra-short-term PV power forecasting is essential for the power system with a high proportion of renewable energy integration, which can provide power fluctuation information hours ahead and help to mitigate the interference of the random PV power output. Most of the PV power forecasting methods mainly focus on employing local ground-based observation data, ignoring the spatial and temporal distribution and correlation characteristics of solar energy and meteorological impact factors. Therefore, a novel ultra-short-term PV power forecasting method based on the satellite image data is proposed in this paper, which combines the spatio-temporal correlation between multiple plants with power and cloud information. The associated neighboring plant is first selected by spatial-temporal cross-correlation analysis. Then the global distribution information of the cloud is extracted from satellite images as additional inputs with other general meteorological and power inputs to train the forecasting model. The proposed method is compared with several benchmark methods without considering the information of neighboring plants. Results show that the proposed method outperforms the benchmark methods and achieves a higher accuracy at 4.73%, 10.54%, and 4.88%, 11.04% for two target PV plants on a four-month validation dataset, in terms of root mean squared error and mean absolute error value, respectively.

Suggested Citation

  • Wang, Fei & Lu, Xiaoxing & Mei, Shengwei & Su, Ying & Zhen, Zhao & Zou, Zubing & Zhang, Xuemin & Yin, Rui & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant," Energy, Elsevier, vol. 238(PC).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pc:s0360544221021940
    DOI: 10.1016/j.energy.2021.121946
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    6. Wang, Fei & Tong, Shuang & Sun, Yiqian & Xie, Yongsheng & Zhen, Zhao & Li, Guoqing & Cao, Chunmei & Duić, Neven & Liu, Dagui, 2022. "Wind process pattern forecasting based ultra-short-term wind speed hybrid prediction," Energy, Elsevier, vol. 255(C).
    7. Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
    8. Alessandro Niccolai & Emanuele Ogliari & Alfredo Nespoli & Riccardo Zich & Valentina Vanetti, 2022. "Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection," Energies, MDPI, vol. 15(24), pages 1-16, December.
    9. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    10. Feng, Zhong-kai & Huang, Qing-qing & Niu, Wen-jing & Yang, Tao & Wang, Jia-yang & Wen, Shi-ping, 2022. "Multi-step-ahead solar output time series prediction with gate recurrent unit neural network using data decomposition and cooperation search algorithm," Energy, Elsevier, vol. 261(PA).
    11. Rosen, Karol & Angeles-Camacho, César & Elvira, Víctor & Guillén-Burguete, Servio Tulio, 2023. "Intra-hour photovoltaic forecasting through a time-varying Markov switching model," Energy, Elsevier, vol. 278(PB).
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