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

Nao Kumekawa, Hayato Honma, Shinji Wakao

    研究成果: Conference contribution

    抜粋

    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.

    元の言語English
    ホスト出版物のタイトル2017 IEEE 44th Photovoltaic Specialist Conference, PVSC 2017
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ1-6
    ページ数6
    ISBN(電子版)9781509056057
    DOI
    出版物ステータスPublished - 2018 5 25
    イベント44th IEEE Photovoltaic Specialist Conference, PVSC 2017 - Washington, United States
    継続期間: 2017 6 252017 6 30

    Other

    Other44th IEEE Photovoltaic Specialist Conference, PVSC 2017
    United States
    Washington
    期間17/6/2517/6/30

    ASJC Scopus subject areas

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

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  • これを引用

    Kumekawa, N., Honma, H., & Wakao, S. (2018). Prediction interval estimation of 10 second fluctuation of PV output with just-in-time modeling. : 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