Real-time monitoring and prediction method of commercial building fire temperature field based on distributed optical fiber sensor temperature measurement system

Abstract

In order to accomplish the task of real-time monitoring and prediction of fire temperature in commercial buildings, this study proposes a sensor arrangement method based on sensor and fire field characteristics to better reflect the fire temperature changes of building space, and conducts high temperature sensor measurement experiments based on this method. Then the study carries out fire dynamics simulations to establish a temperature change dataset containing fire and sensor temperature changes. The study establishes several prediction models using different algorithms based on the dataset, and the models built by artificial neutral network (ANN) and long short-term memory (LSTM) respectively have the best performance. The results show that the percentage error between the output temperature of the ANN mapping model and the actual temperature of the fire key plane is 1.94%. The percentage error between the output temperature of the LSTM temperature prediction model and the actual temperature is 1.03%, which satisfies the demand for early warning of the fire temperature hazard. This study also validates the generalization of the two models for fire temperature prediction in different spatial areas, and the method can provide a good solution for real-time monitoring and early warning of fire temperature in commercial buildings.