An Approach for the Electricity Consumption Prediction based on Artificial Neural Network

DInh Hoa Nguyen, Anh Tung Nguyen

    研究成果: Chapter in Book/Report/Conference proceedingConference contribution

    抄録

    This paper studies the day-ahead prediction of electricity consumption for power supply-demand balance in electric power networks. To handle the uncertainties in weather forecast and the nonlinearity relation between the electricity consumption and the weather conditions, this paper proposes a Radial Basis Function like Artificial Neural Network (RBF-like ANN) model with temperature, humidity, and sampling times as inputs. Then the Least Absolute Deviation, i.e., the L1 norm condition, is employed as the optimization cost which is minimized in the model training process. To solve the L1 optimization problem, two approaches, namely least square (L2) based and alternating direction method of multipliers (ADMM), are utilized and compared. The simulations on real data collected in California shows that the latter approach performs better, and the number of neurons does not affect much to the prediction performance of the latter approach while it does influence on that of the former approach. Further, the proposed RBF-like ANN model equipped with ADMM solving approach provides reasonably good prediction of the electricity consumption in spite of the imprecise weather forecast.

    本文言語英語
    ホスト出版物のタイトルProceedings of 2019 SICE International Symposium on Control Systems, SICE ISCS 2019
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ78-83
    ページ数6
    ISBN(電子版)9784907764623
    DOI
    出版ステータス出版済み - 3 2019
    イベント2019 SICE International Symposium on Control Systems, SICE ISCS 2019 - Kumamoto, 日本
    継続期間: 3 7 20193 9 2019

    出版物シリーズ

    名前Proceedings of 2019 SICE International Symposium on Control Systems, SICE ISCS 2019

    会議

    会議2019 SICE International Symposium on Control Systems, SICE ISCS 2019
    国/地域日本
    CityKumamoto
    Period3/7/193/9/19

    All Science Journal Classification (ASJC) codes

    • 制御およびシステム工学
    • 電子工学および電気工学
    • 機械工学
    • 制御と最適化

    フィンガープリント

    「An Approach for the Electricity Consumption Prediction based on Artificial Neural Network」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

    引用スタイル