Short-term prediction of electric demand in building sector via hybrid support vector regression

Reliable and highly-generalized prediction models of short-term electric demand are urgently needed for the building sector, as the crucial basis of sophisticated building energy management. Advances in metering technologies and machine learning methods provide both opportunities and challenges for modified approaches. With multi-resolution wavelet decomposition (MWD) as a preprocessing from the point of view of signal analysis, the hybrid support vector regression (SVR) model was applied in two case study buildings to predict the hourly electric demand intensity. Taking ten-dimensional parameters of 29 workdays as the training sample, this model was carried out in a mall and a hotel, the consumed electric demand sequences of which represented the stationary and non-stationary series respectively. By comparisons between the hybrid SVR and the pure SVR, results indicated that the introduction of MWD can always improve the predicting accuracy for the hotel, while it is not necessary for the mall. Specifically, the similar steady level around 0.65 W/m(2) of absolute error was obtained for the mall and the hotel buildings, when a was lower than 0.1. At the same time, the steady quantitative values of relative errors tended to be around 4% and 6% respectively for the hotel and the mall. Based on the limited historical readings, this paper offers an on-line prediction method of short-term electric demand, which is applicable for the further smart energy management. (C) 2017 Elsevier Ltd. All rights reserved.