Short-term load forecasting in non-residential Buildings

Abstract

Short-term load forecasting (STLF) has become an essential tool in the electricity sector. It has been object of vast research since energy load is known to be non-linear and, therefore, very difficult to predict with accuracy. We focus here on non-residential building STLF, an special case of STLF where weather shows smaller influence on the load than in normal scenarios and forecast models, contrary to those on the literature, are required to be simple, avoiding dull and complicated trial-and-error parametrisation or setting-up processes. Under these premises, we have used a two-step methodology comprising a classification and a adjustment steps. Since the non-linearity of the load is associated to the activity in the building, we have demonstrated that the best way to deal with it is using the work day schedule as day-type classifier. Moreover, we have evaluated a number of statistical methods and Artificial Intelligence methods to adjust the typical hourly consumption curve, concluding that an autoregressive time series suffices to fulfil the requirements, even in a 5 day-ahead horizon.