基于LSTM的地铁车站设备间设备发热量预测OA

In the design of air conditioning systems for subway station equipment rooms,the absence of reliable heat generation data can impact the precision of the design and the effectiveness of energy conservation and emission reduction.By integrating actual measured heat generation data from equipment in subway station equipment rooms and conducting a quantitative analysis of the influence of characteristic variables on the heat generation changes in these rooms,five stable characteristic variables and nine time-varying characteristic variables for predicting equipment heat generation in subway station equipment rooms are identified.Considering the nonlinear and time-series correlation characteristics of the equipment heat generation data in subway station equipment rooms,a prediction method for equipment heat generation based on Long Short-Term Memory(LSTM)neural networks is proposed.This method significantly enhances the accuracy of predicting equipment heat generation in subway station equipment rooms,providing a reference for future solutions to issues such as the design of subway station air conditioning systems,energy-saving control,and intelligent control.