Energy consumption detection method for high-rise prefabricated green buildings based on mobile sensor networks

The existing building energy consumption detection methods mostly use smart meters to obtain energy consumption data, but they can only provide overall energy consumption data and cannot analyze the energy consumption of each sub item equipment in detail. Research on energy consumption detection methods for high-rise prefabricated green buildings based on mobile sensor networks. Firstly, a tree network structure is established based on ZigBee technology for network communication, and a sensor network node and channel model is established to improve communication efficiency. Then, the collected building energy consumption data was preprocessed using the K-center clustering algorithm. By selecting the measured energy consumption point closest to the average energy consumption value as the clustering center, the interference of outliers on the clustering results was effectively reduced, significantly improving the data quality. Finally, the accurate detection of green building energy consumption was achieved using local outlier factor detection algorithm. The experimental results show that the data transmission integrity of the method proposed in this paper is always above 95%, the detection accuracy is always above 95%, the response time is stable between 5-9 seconds, the fluctuation is small, and it exhibits high stability.