Combination of cuckoo search and wavelet neural network for midterm building energy forecast

The electrical load prediction for buildings plays a critical role in the smart-grid paradigm, since accurate predictions provide efficient energy management. A synthetic approach has been used in two buildings as the case studies with wavelet neural network (WNN) as a preparation for a process from the signal assessment perspective to forecast the density of electricity requirements. In this paper, singular spectrum analysis (SSA) and WNN based forecast engine have been considered. In this model, the free parameters of WNN are tuned optimally by cuckoo search (CS) algorithm. Using parameters of ten-dimensional variables of 29 weekdays as learning samples, this technique has been performed in a hotel and a mall, where the used electricity pattern respectively denoted dynamic and static series. By comparing the proposed approach with other models, it can be claim that WNN can generally enhance the forecasting precision for the hotel, although it is not essential for the mall. Particularly, the analogous stable amount that is about 0.65 W/m2of absolute error was gotten for the mall and the hotel buildings, where ε was less than 0.1. Simultaneously, the stable quantitative magnitudes of relative errors were around 4% and 6% for the mall and the hotel, respectively. Using the brief historical measurement, real-time forecasting approach of interim electricity requirement is proposed which can be applied to have smarter energy control.