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Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation

Author

Listed:
  • Taiki Kure

    (Graduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, Japan)

  • Haruka Danil Tsuchiya

    (Graduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, Japan)

  • Yusuke Kameda

    (Faculty of Science and Technology, Sophia University, Tokyo 102-8554, Japan)

  • Hiroki Yamamoto

    (Graduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, Japan)

  • Daisuke Kodaira

    (Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba 305-8573, Japan)

  • Junji Kondoh

    (Graduate School of Science and Technology, Tokyo University of Science, Noda 278-8510, Japan)

Abstract

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method had been proposed previously for multiple PV systems using motion estimation. This method forecasts the short time ahead PV power generation by estimating the motion between two geographical images of the distributed PV power systems. In this method, the parameter λ , which relates the smoothness of the resulting motion vector field and affects the accuracy of the forecasting, is important. This study focuses on the parameter λ and evaluates the effect of changing this parameter on forecasting accuracy. In the periods with drastic power output changes, the forecasting was conducted on 101 PV systems. The results indicate that the absolute mean error of the proposed method with the best parameter is 10.3%, whereas that of the persistence forecasting method is 23.7%. Therefore, the proposed method is effective in forecasting periods when PV output changes drastically within a short time interval.

Suggested Citation

  • Taiki Kure & Haruka Danil Tsuchiya & Yusuke Kameda & Hiroki Yamamoto & Daisuke Kodaira & Junji Kondoh, 2022. "Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation," Energies, MDPI, vol. 15(8), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2855-:d:793339
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    References listed on IDEAS

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    1. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    2. Yosui Miyazaki & Yusuke Kameda & Junji Kondoh, 2019. "A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets," Energies, MDPI, vol. 12(24), pages 1-14, December.
    3. Ueckerdt, Falko & Brecha, Robert & Luderer, Gunnar, 2015. "Analyzing major challenges of wind and solar variability in power systems," Renewable Energy, Elsevier, vol. 81(C), pages 1-10.
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