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

作者：Borges Cruz E^*; Penya Yoseba K; Fernandez Ivan; Prieto Juan; Bretos Oscar

来源：Energies, 2013, 6(4): 2110-2129.

DOI：10.3390/en6042110

• short term load forecasting
• artificial intelligence
• statistical methods

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.

• 出版日期2013-4