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

Cruz E. Borges, Yoseba K. Penya, Iván Fernández, Juan Prieto and Oscar Bretos
Additional contact information
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

Energies, 2013, vol. 6, issue 4, 1-20

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.

Keywords: short term load forecasting; artificial intelligence; statistical methods (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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