Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation

Taiki Kure, Haruka Danil Tsuchiya, Yusuke Kameda, Hiroki Yamamoto, Daisuke Kodaira and Junji Kondoh
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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

Energies, 2022, vol. 15, issue 8, 1-20

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.

Keywords: photovoltaic (PV) power forecast; multiple PV forecasting; short-term PV forecasting; motion estimation; optical flow; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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