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

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...

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Bibliographic Details
Published inEnergies (Basel) Vol. 6; no. 4; pp. 2110 - 2129
Main Authors Borges, Cruz, Penya, Yoseba, Fernández, Iván, Prieto, Juan, Bretos, Oscar
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2013
Subjects
Accuracy
Algorithms
artificial intelligence
Buildings
Demand side management
Energy consumption
Forecasting
HVAC
Public buildings
short term load forecasting
Statistical methods
Support vector machines
Spain
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Summary: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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ISSN:1996-1073
1996-1073
DOI:10.3390/en6042110