Optimal combined short-term building load forecasting

Short-term load forecasting (STLF) is one of the main pillars of the smart grid vision since a reliable prediction helps reducing the deviation in the generation and, consequently, increases the overall efficiency. Classic STLF methods range from statistical models to more complicated Artificial Intelligence approaches. All of them presents remarkable records in a certain situations while simultaneously fail in others and, moreover, each possibility offers different information and precision. In this way, analysing the results of the models gives us the chance to 1) learn which model should be applied when, 2) correct these results and, 3) combine them to obtain a prediction of higher quality. Finally, we focus here in building STLF, an special branch that presents additional requirements, especially regarding the need of simplicity. In this way, we explore these 3 post-process alternatives on the most popular STLF techniques. Specifically, we present here a comparative between 4 forecasting methods and 6 forms of post-processing their results. We have tested all thoroughly with 4 different datasets and shown that, in this problem domain, the best forecasting method can only be improved by post-processing only in case it does not clearly outperform the rest, since all analysed post-processing methods use the precision difference on the methods to correct them.