Forecasting Model of the Electricity Consumption: Preliminary Study using Statistical Data Analysis and Artificial Neural Network (SDA-ANN) Framework

  • Authors

    • Muhammad Asraf H.
    • Nur Dalila K.A.
    • Nor Kamariah Kasmin@Bajuri
    • Zakiah M.Y.
    • Siti Mariam Mohammad Ilyas
    • Nooritawati M.T
    2018-11-30
    https://doi.org/10.14419/ijet.v7i4.22.27864
  • Artificial Neural Network (ANN), electricity consumption, prediction model, statistical data analysis (SDA), forecast

  • Abstract

    There is a growing trend in utilizing forecast model in predicting energy usage in order to pursue sustainable practice. Among the benefit of the prediction model includes energy efficient usage and basic reduction in operating cost. The objective of this paper to employ
    Artificial Neural Network (ANN) to be used as the preliminary forecast model for electricity consumption trend prediction. The proposed ANN model will also include on pre-processing of the input variable through Statistical Data Analysis method to improve on the model. The model is then subjected to case study of electricity consumption in Universiti Teknologi MARA Pasir Gudang (UiTMPG), where the environmental temperature, number of student and staff noted as attendance data is designated as the input variables. Actual monthly billing data and UiTM attendance data is used as the testing data for the ANN, and the forecast model successfully generates the
    prediction equation. Results from the forecast model show the recorded Mean Relative Error (MRE) is within the acceptable value of 3.0254%.

     

     

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  • How to Cite

    Asraf H., M., Dalila K.A., N., Kamariah Kasmin@Bajuri, N., M.Y., Z., Mariam Mohammad Ilyas, S., & M.T, N. (2018). Forecasting Model of the Electricity Consumption: Preliminary Study using Statistical Data Analysis and Artificial Neural Network (SDA-ANN) Framework. International Journal of Engineering & Technology, 7(4.22), 132-136. https://doi.org/10.14419/ijet.v7i4.22.27864

    Received date: 2019-02-24

    Accepted date: 2019-02-24

    Published date: 2018-11-30