Simulation‐data‐driven load disaggregation based on multi‐channel neural network for industrial and commercial users

Abstract

The immeasurable energy-saving potential of industrial parks and commercial buildings with unprecedented growth in scale and quantity can be well exploited by load disaggregation, a promising technique for demand-side refinement management. However, traditional load disaggregation studies mainly focus on residential users and neglect the collection and analysis of industrial and commercial loads. This study employs a simulated response model of versatile load categories with voltage-dependent load functions to overcome the unavailability of industrial and commercial datasets. Specially, a weighting factors allocation method considering probability statistics of power consumption distribution characteristics is proposed to construct a typical daily multi-type load aggregation model. Furthermore, an intelligent load disaggregation strategy is developed based on a multi-channel neural network to constructively improve multi-feature tracking and convergence efficiency. Finally, the simulation results of the detailed study case confirm the tractability and effectiveness of the proposed approach to carry out load identification among multi-type users, with an accuracy of 94.9% and errors of individual categories controlled between 3.32% and 10.686%. Moreover, additional interference tests demonstrate that the developed model has superiority over common load disaggregation structures, with better tolerance to exceptional voltage fluctuation and noise.