A Machine Learning-based Approach for the Prediction of Electricity Consumption

Dinh Hoa Nguyen, Anh Tung Nguyen

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

    3 Citations (Scopus)


    Balancing the power supply and demand is one of the most fundamental and important problems for the operation and control of any electric power grid. There are multiple ways to guarantee the supply-demand balance, but in this research we focus on one specific method to facilitate it namely the prediction of electricity consumption, which is widely used by utility companies or system operators. It is known that this prediction is challenging because of many reasons, for example, inexact weather forecasts, uncertain consumers' behaviors, etc. Hence, analytical and linear models of electricity consumption might not be able to deal with such issues well. This paper therefore presents a machine learning-based approach to predict electricity consumption, in which an improved radial basis function neural network (iRBF-NN) is proposed, whose inputs are time sampling points, temperature, and humidity associated with the consumption. The parameters of this iRBF-NN are sought by solving an optimization problem where four types of cost functions are used and compared on their performances and computational costs. Afterward, the derived model is employed to predict the future electricity consumption based on the hourly forecasts of temperature and humidity. Finally, simulation results for realistic data in Tokyo are presented to illustrate the efficiency of the proposed approach.

    Original languageEnglish
    Title of host publication2019 12th Asian Control Conference, ASCC 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages6
    ISBN (Electronic)9784888983006
    Publication statusPublished - Jun 2019
    Event12th Asian Control Conference, ASCC 2019 - Kitakyushu-shi, Japan
    Duration: Jun 9 2019Jun 12 2019

    Publication series

    Name2019 12th Asian Control Conference, ASCC 2019


    Conference12th Asian Control Conference, ASCC 2019

    All Science Journal Classification (ASJC) codes

    • Computer Science Applications
    • Control and Optimization
    • Mechanical Engineering


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