The most important source of energy consumption in public buildings is the energy use of air conditioning system, which is characterized by multiple working conditions, multiple parameters and multiple disturbances. In order to accurately reflect the energy consumption characteristics of air conditioning systems in public buildings, this paper uses the PSO particle swarm algorithm to optimize the ELMAN neural network to establish the prediction model of air conditioning systems in public buildings. The results of the case analysis show that the prediction accuracy of the PSO-ELMAN neural network prediction model is significantly improved compared with the ELMAN neural network, which is a reference value for realizing the research of energy consumption prediction of air conditioning system in public buildings.
KEYWORDS: Fire, Telecommunications, Data modeling, Wireless communications, Design and modelling, Data transmission, Clouds, Neural networks, Network architectures, Data communications
At present, Chinese electrical fire prevention and control is still in the development stage. The existing electrical fire monitoring products have technical defects, the false alarm rate is high, and the monitoring effect on electrical fire is limited. Because of these situations, the microcontroller STM32WLE5CCU6 and the sharp micro power quality chip RN8302B is used as the core, combined with the LSTM neural network, an intelligent front-end device with real-time monitoring of electrical fire data and data depth processing functions are designed, which realizes the monitoring and prediction of an electrical fire. And composed an intelligent electrical fire warning system capable of wireless communication and information interaction with intelligent front-end equipment, LoRa wireless communication, RS485 communication, and cloud platform.
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