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

DInh Hoa Nguyen, Anh Tung Nguyen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings of 2019 SICE International Symposium on Control Systems, SICE ISCS 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages78-83
    Number of pages6
    ISBN (Electronic)9784907764623
    DOIs
    Publication statusPublished - Mar 2019
    Event2019 SICE International Symposium on Control Systems, SICE ISCS 2019 - Kumamoto, Japan
    Duration: Mar 7 2019Mar 9 2019

    Publication series

    NameProceedings of 2019 SICE International Symposium on Control Systems, SICE ISCS 2019

    Conference

    Conference2019 SICE International Symposium on Control Systems, SICE ISCS 2019
    CountryJapan
    CityKumamoto
    Period3/7/193/9/19

    All Science Journal Classification (ASJC) codes

    • Control and Systems Engineering
    • Electrical and Electronic Engineering
    • Mechanical Engineering
    • Control and Optimization

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