Realistic scenario modelling for building power supply and distribution system based on non-intrusive load monitoring

Abstract

In power supply and distribution system in buildings, the conventional designs of loads are fictitious, and it is difficult to find the vulnerabilities timely. In order to solve this problem, a realistic scenario modelling method is proposed. Aiming at the input of the power consumption data in realistic scenario model, a non-intrusive load monitoring method is used, combined with sliding window switching event detection method of electrical appliance, a sequence to short-sequence deep learning model is also established whose input vectors are composed of switching time and total power data. The input vectors in the deep learning model can be decomposed to the individual electrical appliance. Compared with CO and FHMM algorithm, the decomposition results of this model are more excellent in precision, recall, F1 score and accuracy. And it is more practical and accurate to replace the estimated data with the electricity consumption data obtained by NILM in the realistic scenario modelling.