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.
Highlights
Load forecasting is an essential part of the scheduling, management and operation of a power system.Since electrical energy cannot be stored, it is important to deliver an accurate prediction in order to avoid dispatch problems due to unexpected loads
Short-term load forecasting (STLF) is the prediction of energy demand in a time-span ranging from minutes to several days, being crucial for several smart grid applications
This paper focuses on building STLF, a particular case dealing with issuing day-ahead energy consumption predictions in non-residential buildings such as schools, universities, public buildings or companies’ facilities
Summary
Load forecasting is an essential part of the scheduling, management and operation of a power system. Since electrical energy cannot be stored, it is important to deliver an accurate prediction in order to avoid dispatch problems due to unexpected loads. Short-term load forecasting (STLF) is the prediction of energy demand in a time-span ranging from minutes to several days, being crucial for several smart grid applications. As discussed above, it is important for the economic and secure operation of power grids, but several factors should be considered as well. The publication of the energy consumption and its conversion to equivalent CO2 emissions decreases the load that affects future predictions due to its influence on social consciousness
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