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

DeustoTech-Deusto Technology Foundation, Energy Unit, University of Deusto, Avenida de las Universidades 24, Bilbao 48007, Basque Country, Spain
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
Author to whom correspondence should be addressed.
Energies 2013, 6(4), 2110-2129;
Received: 18 January 2013 / Revised: 12 March 2013 / Accepted: 18 March 2013 / Published: 17 April 2013
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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. View Full-Text
Keywords: short term load forecasting; artificial intelligence; statistical methods short term load forecasting; artificial intelligence; statistical methods
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MDPI and ACS Style

Borges, C.E.; Penya, Y.K.; Fernández, I.; Prieto, J.; Bretos, O. Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings. Energies 2013, 6, 2110-2129.

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