Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Parameter Optimization of Motion Estimation for Geographically Distributed Photovoltaic Power Forecasting

Version 1 : Received: 27 January 2022 / Approved: 10 February 2022 / Online: 10 February 2022 (02:22:32 CET)

A peer-reviewed article of this Preprint also exists.

Kure, T.; Tsuchiya, H.D.; Kameda, Y.; Yamamoto, H.; Kodaira, D.; Kondoh, J. Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation. Energies 2022, 15, 2855. Kure, T.; Tsuchiya, H.D.; Kameda, Y.; Yamamoto, H.; Kodaira, D.; Kondoh, J. Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation. Energies 2022, 15, 2855.

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 has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.

Keywords

photovoltaic (PV) power forecast; multiple PV forecasting; short-term PV forecasting; motion estimation; optical flow; smart grid

Subject

Engineering, Electrical and Electronic Engineering

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