Neural Network and Fuzzy System Based Classification Method for Building Electrical Equipment

The identification and classification of building electrical equipment, as an important part of building energy management, can enable managers to fully understand the operation of equipment, thereby reducing unnecessary energy consumption. However, the existing methods have low classification and recognition accuracy in complex environments. Therefore, in order to further improve performance, this paper proposes a novel neural network and fuzzy system based building electrical equipment classification method, which extracts equipment features through graph neural networks and outputs the recognition results according to corresponding rules. Through comparative experiments on real-world datasets, the method in this paper has achieved better recognition performance.