Research on the Prediction of Energy-saving Potential of Traffic Tunnel Based on BP Neural Network

Accurate grasp of underground building environmental temperature and humidity and cooling and heating loads is the basis for energy-saving analysis, ventilation and air conditioning system design and operation control. In order to come up with a method that can quickly and accurately predict the environmental temperature and humidity and energy-saving potential of underground power station traffic tunnel, this paper takes Xiangyou pumped storage power station traffic tunnel as the research object and designs a BP neural network model for traffic tunnel temperature and relative humidity prediction with respect to the main environmental influencing factors. Comparing with the test data, it is verified that the model has high prediction accuracy. At the same time, the prediction model was used to calculate the energy saving potential of traffic tunnel ventilation. The results showed that the relative error of predicting the energy saving potential of traffic tunnel in summer was less than 2% and in winter was less than 0.3%. This prediction method can obtain the traffic tunnel air temperature and humidity and ventilation energy saving potential simply and accurately, so that the design can be developed in the direction favorable to the energy saving of underground buildings.