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    24-h electrical load data — a sequential or partitioned time series?

    Fay, Damien and Ringwood, John and Condon, Marissa and Kelly, Michael (2003) 24-h electrical load data — a sequential or partitioned time series? Neurocomputing, 55 (3-4). pp. 469-498. ISSN 0925-2312

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    Variations in electrical load are, among other things, hour of the day dependent, introducing a dilemma for the forecaster: whether to partition the data and use a separate model for each hour of the day (the parallel approach), or use a single model (the sequential approach). This paper examines which approach is appropriate for forecasting hourly electrical load in Ireland. It is found that, with the exception of some hours of the day, the sequential approach is superior. The final solution however, uses a combination of linear sequential and parallel neural models in a multi-time scale formulation.

    Item Type: Article
    Keywords: Load forecasting; Time series analysis; Multi-layer perceptrons; Principal component analysis;
    Academic Unit: Faculty of Science and Engineering > Electronic Engineering
    Item ID: 9504
    Identification Number:
    Depositing User: Professor John Ringwood
    Date Deposited: 01 Jun 2018 14:05
    Journal or Publication Title: Neurocomputing
    Publisher: Elsevier
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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