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    Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression

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    Type
    Article
    Authors
    Taieb, Souhaib Ben
    Huser, Raphaël cc
    Hyndman, Rob J.
    Genton, Marc G. cc
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Statistics Program
    Date
    2016-03-02
    Online Publication Date
    2016-03-02
    Print Publication Date
    2016-09
    Permanent link to this record
    http://hdl.handle.net/10754/622549
    
    Metadata
    Show full item record
    Abstract
    Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared with traditional electricity data based on aggregated consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand in order to undertake appropriate planning of generation and distribution. We propose to estimate an additive quantile regression model for a set of quantiles of the future distribution using a boosting procedure. By doing so, we can benefit from flexible and interpretable models, which include an automatic variable selection. We compare our approach with three benchmark methods on both aggregated and disaggregated scales using a smart meter data set collected from 3639 households in Ireland at 30-min intervals over a period of 1.5 years. The empirical results demonstrate that our approach based on quantile regression provides better forecast accuracy for disaggregated demand, while the traditional approach based on a normality assumption (possibly after an appropriate Box-Cox transformation) is a better approximation for aggregated demand. These results are particularly useful since more energy data will become available at the disaggregated level in the future.
    Citation
    Ben Taieb S, Huser R, Hyndman RJ, Genton MG (2016) Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression. IEEE Transactions on Smart Grid 7: 2448–2455. Available: http://dx.doi.org/10.1109/TSG.2016.2527820.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Smart Grid
    DOI
    10.1109/TSG.2016.2527820
    ae974a485f413a2113503eed53cd6c53
    10.1109/TSG.2016.2527820
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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