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    Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks


    Ringwood, John and Bofelli, D. and Murray, F.T. (2001) Forecasting Electricity Demand on Short, Medium and Long Time Scales Using Neural Networks. Journal of Intelligent and Robotic Systems, 31 (1-3). pp. 129-147. ISSN 0921-0296

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    Abstract

    This paper examines the application of artificial neural networks (ANNs) to the modelling and forecasting of electricity demand experienced by an electricity supplier. The data used in the application examples relates to the national electricity demand in the Republic of Ireland, generously supplied by the Electricity Supply Board (ESB). The paper focusses on three different time scales of interest to power boards: yearly (up to fifteen years in advance), weekly (up to three years in advance) and hourly (up to 24 h ahead). Electricity demand exhibits considerably different characteristics on these different time scales, both in terms of the underlying autoregressive processes and the causal inputs appropriate to each time scale. Where possible, the ANN-based models draw on the applications experience gained with linear modelling techniques and in one particular case, manual forecasting methods.

    Item Type: Article
    Keywords: artificial neural networks; electrical load; electricity demand; load forecasting; Box–Jenkins model;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 8833
    Depositing User: Professor John Ringwood
    Date Deposited: 19 Sep 2017 15:23
    Journal or Publication Title: Journal of Intelligent and Robotic Systems
    Publisher: Springer
    Refereed: Yes
    URI:

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