A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets

Yosui Miyazaki, Yusuke Kameda and Junji Kondoh
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Yosui Miyazaki: Graduate School of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan
Yusuke Kameda: Graduate School of Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan
Junji Kondoh: Graduate School of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan

Energies, 2019, vol. 12, issue 24, 1-14

Abstract: The number of photovoltaic (PV) power systems being installed worldwide has been increasing. This has resulted in maintenance of an adequate balance between demand and supply becoming a great concern for power system operators. Forecasting PV power outputs is a promising countermeasure that has been garnering significant interest. Conventional methods for achieving this often use learning methods, such as neural networks and support vector regression. In contrast, this paper proposes a short-term power-forecasting method for geographically distributed PV systems that uses only their previous output power data. In the proposed method, first, the ratio of the power generation output to the maximum power output value for each observation instance in a designated period for each PV system at a certain date and time is obtained. Then, the future geographical distribution of the ratio is predicted from the temporal change (motion) of the preceding distribution. Finally, the predicted ratio is reconverted into the power output to perform short-term power forecasting. The results of total PV output power prediction in the Kanto area of Japan indicate that the proposed method has an average mean absolute percentage error of 4.23% and root mean square error of 0.69 kW, which verifies its efficacy.

Keywords: geographical distribution; motion estimation; output forecast; photovoltaic power; solar energy (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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