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Spatial-temporal characteristics analysis of solar irradiance forecast errors in Europe and North America

Author

Listed:
  • Bai, Mingliang
  • Yao, Peng
  • Dong, Haiyu
  • Fang, Zuliang
  • Jin, Weixin
  • Xusheng Yang,
  • Liu, Jinfu
  • Yu, Daren

Abstract

Accurate photovoltaic (PV) power forecast is crucial to power systems. Solar irradiance forecast is the fundamentals of PV power forecast. Current researches merely focus on improving the forecast accuracy. There hasn't been comprehensive study on the spatial-temporal characteristics of solar irradiance forecast errors. Thus, this paper analyzed the error characteristics of solar irradiance forecast provided by European Centre for Medium-Range Weather Forecasts (ECMWF), one of the most accurate numerical weather prediction (NWP) products. ECMWF's forecasts were compared with ERA5 solar irradiance reanalysis data to reveal the error distribution characteristics. Four-year (2017–2020) data from the geographical region bounded by 63°N, −126°W, 21°S, and 36°E (covering most parts of Europe and North America) were studied. Experiments show that solar irradiance forecast errors peak at noon of the local time. Furthermore, correlations between solar irradiance forecast errors and other weather variables were also revealed. Experiments show that solar irradiance forecast errors have negative correlation with low cloud cover forecast errors and positive correlation with air temperature forecast errors. The possible reasons for the correlation relationship were also analyzed in detail. This paper systematically reveals the spatial-temporal characteristics of solar irradiance forecast errors and provides a useful guideline for solar PV system operation.

Suggested Citation

  • Bai, Mingliang & Yao, Peng & Dong, Haiyu & Fang, Zuliang & Jin, Weixin & Xusheng Yang, & Liu, Jinfu & Yu, Daren, 2024. "Spatial-temporal characteristics analysis of solar irradiance forecast errors in Europe and North America," Energy, Elsevier, vol. 297(C).
  • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224009605
    DOI: 10.1016/j.energy.2024.131187
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    References listed on IDEAS

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    1. Yang, Weijia & Sparrow, Sarah N. & Ashtine, Masaō & Wallom, David C.H. & Morstyn, Thomas, 2022. "Resilient by design: Preventing wildfires and blackouts with microgrids," Applied Energy, Elsevier, vol. 313(C).
    2. Suman Ravuri & Karel Lenc & Matthew Willson & Dmitry Kangin & Remi Lam & Piotr Mirowski & Megan Fitzsimons & Maria Athanassiadou & Sheleem Kashem & Sam Madge & Rachel Prudden & Amol Mandhane & Aidan C, 2021. "Skilful precipitation nowcasting using deep generative models of radar," Nature, Nature, vol. 597(7878), pages 672-677, September.
    3. Zhang, Liwenbo & Wilson, Robin & Sumner, Mark & Wu, Yupeng, 2023. "Advanced multimodal fusion method for very short-term solar irradiance forecasting using sky images and meteorological data: A gate and transformer mechanism approach," Renewable Energy, Elsevier, vol. 216(C).
    4. Voyant, Cyril & Notton, Gilles & Duchaud, Jean-Laurent & Gutiérrez, Luis Antonio García & Bright, Jamie M. & Yang, Dazhi, 2022. "Benchmarks for solar radiation time series forecasting," Renewable Energy, Elsevier, vol. 191(C), pages 747-762.
    5. Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
    6. Qi, Ning & Cheng, Lin & Xu, Helin & Wu, Kuihua & Li, XuLiang & Wang, Yanshuo & Liu, Rui, 2020. "Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads," Applied Energy, Elsevier, vol. 279(C).
    Full references (including those not matched with items on IDEAS)

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