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Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

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  • Cruz E. Borges

    ()
    (DeustoTech-Deusto Technology Foundation, Energy Unit, University of Deusto, Avenida de las Universidades 24, Bilbao 48007, Basque Country, Spain)

  • Yoseba K. Penya

    ()
    (DeustoTech-Deusto Technology Foundation, Energy Unit, University of Deusto, Avenida de las Universidades 24, Bilbao 48007, Basque Country, Spain)

  • Iván Fernández

    ()
    (DeustoTech-Deusto Technology Foundation, Energy Unit, University of Deusto, Avenida de las Universidades 24, Bilbao 48007, Basque Country, Spain)

  • Juan Prieto

    ()
    (Indra, Smart Energy Department, Optimisation and Prevision Area, Parque empresarial Arroyo de la Vega, edificio Violeta 2, Avenida de Bruselas 35, Alcobendas, Madrid 28108, Spain)

  • Oscar Bretos

    ()
    (Indra, Smart Energy Department, Optimisation and Prevision Area, Parque empresarial Arroyo de la Vega, edificio Violeta 2, Avenida de Bruselas 35, Alcobendas, Madrid 28108, Spain)

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    Abstract

    Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones.

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    Bibliographic Info

    Article provided by MDPI, Open Access Journal in its journal Energies.

    Volume (Year): 6 (2013)
    Issue (Month): 4 (April)
    Pages: 2110-2129

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    Handle: RePEc:gam:jeners:v:6:y:2013:i:4:p:2110-2129:d:25082

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    Web page: http://www.mdpi.com/

    Related research

    Keywords: short term load forecasting; artificial intelligence; statistical methods;

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    1. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    2. Rob J. Hyndman & Anne B. Koehler, 2005. "Another Look at Measures of Forecast Accuracy," Monash Econometrics and Business Statistics Working Papers 13/05, Monash University, Department of Econometrics and Business Statistics.
    3. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    4. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    5. Armstrong, J. Scott, 1989. "Combining forecasts: The end of the beginning or the beginning of the end?," International Journal of Forecasting, Elsevier, vol. 5(4), pages 585-588.
    6. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    7. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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