Prediction interval estimation of 10 second fluctuation of PV output with just-in-time modeling

Nao Kumekawa; Hayato Honma; Shinji Wakao

doi:10.1109/PVSC.2017.8366397

Prediction interval estimation of 10 second fluctuation of PV output with just-in-time modeling

Nao Kumekawa, Hayato Honma, Shinji Wakao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The Output of photovoltaic (PV) systems depends on weather conditions. Therefore if there is a large introduction of PV systems, the power quality in the distribution system will be affected. One effective solution for this problem is to predict PV output. Although the need for prediction information for short period fluctuation is increasing, it is difficult to directly predict a steep fluctuation on the second time scale. For the prediction information of PV output, we propose the estimation of the prediction interval of the fluctuation widths on a 10 second scale. In this paper, we carry out the prediction by using the conventional method, with one-dimensional kernel density estimation, and the proposed method, with two-dimensional kernel density estimation. Then, we discuss the effectiveness of the proposed method based on several numerical indexes.

Original language	English
Title of host publication	2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781509056057
DOIs	https://doi.org/10.1109/PVSC.2017.8366397
Publication status	Published - 2018 May 25
Event	44th IEEE Photovoltaic Specialist Conference, PVSC 2017 - Washington, United States Duration: 2017 Jun 25 → 2017 Jun 30

Other

Other	44th IEEE Photovoltaic Specialist Conference, PVSC 2017
Country	United States
City	Washington
Period	17/6/25 → 17/6/30

ASJC Scopus subject areas

Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Electronic, Optical and Magnetic Materials

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Kumekawa, N., Honma, H., & Wakao, S. (2018). Prediction interval estimation of 10 second fluctuation of PV output with just-in-time modeling. In 2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017 (pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PVSC.2017.8366397

Kumekawa, N, Honma, H & Wakao, S 2018, Prediction interval estimation of 10 second fluctuation of PV output with just-in-time modeling. in 2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017. Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 44th IEEE Photovoltaic Specialist Conference, PVSC 2017, Washington, United States, 17/6/25. https://doi.org/10.1109/PVSC.2017.8366397

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abstract = "The Output of photovoltaic (PV) systems depends on weather conditions. Therefore if there is a large introduction of PV systems, the power quality in the distribution system will be affected. One effective solution for this problem is to predict PV output. Although the need for prediction information for short period fluctuation is increasing, it is difficult to directly predict a steep fluctuation on the second time scale. For the prediction information of PV output, we propose the estimation of the prediction interval of the fluctuation widths on a 10 second scale. In this paper, we carry out the prediction by using the conventional method, with one-dimensional kernel density estimation, and the proposed method, with two-dimensional kernel density estimation. Then, we discuss the effectiveness of the proposed method based on several numerical indexes.",

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AB - The Output of photovoltaic (PV) systems depends on weather conditions. Therefore if there is a large introduction of PV systems, the power quality in the distribution system will be affected. One effective solution for this problem is to predict PV output. Although the need for prediction information for short period fluctuation is increasing, it is difficult to directly predict a steep fluctuation on the second time scale. For the prediction information of PV output, we propose the estimation of the prediction interval of the fluctuation widths on a 10 second scale. In this paper, we carry out the prediction by using the conventional method, with one-dimensional kernel density estimation, and the proposed method, with two-dimensional kernel density estimation. Then, we discuss the effectiveness of the proposed method based on several numerical indexes.

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