User Side Valve Opening Prediction of Public Building Heating System Based on Apriori-CNN-LSTM
A CNN-LSTM hybrid neural network user valve opening prediction model based on Apriori association min-ing feature variables is proposed to address the issue of human resource waste caused by relying on manual experi-ence to adjust user valve opening in public building heating systems,which makes valve adjustment more intelligent and efficient.This article first exports historical data of the manually adjusted heating system operation in the early stage from the system.After data cleaning,the Apriori association rule algorithm is used to mine the relationship be-tween variable data and the opening of the regulating valve,forming an association rule library.By analyzing the strong association rules related to the opening of the regulating valve,characteristic variables are selected.Based on the characteristics of the data,a CNN-LSTM hybrid model combined with convolutional neural networks was selected to predict the opening of the regulating valve.Comparison of calculation errors revealed that the prediction results were superior to the single LSTM and Attention-LSTM neural network models.By using neural network algorithms to predict valve opening and achieve intelligent regulation,the dynamic balance between branch flow and heat in the pipeline network is ensured,meeting the user's requirements for heating comfort.
heating systemApriori association rule algorithmLSTM neural networkconvolutional neural networksAttention mechanism
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