Building Energy Consumption Prediction Based on AP Clustering and CNN-BILSTM
Building energy consumption accounts for nearly 40%of the total energy consumption in society,and building industry is one of the industries with the highest energy consumption in China.In order to reflect the operational characteristics of buildings and reduce carbon emissions,a hybrid building energy consumption prediction model based on Affinity Promotion(AP)clustering and CNN-BILSTM neural network is proposed.The factors that affect building energy consumption are clustered through AP clustering.In the CNN-BILSTM model,convolutional neural networks can extract deep level features from building time series consumption data.The flow of information in BILSTM is bidirectional and can effectively handle the relationships between energy consumption data.The experimental results show that compared to other models,CNN-BILSTM performs better in evaluation indicators and pre-dicts more accurately.
AP clusteringConvolutional Neural NetworksBILSTMbuilding energy consumption
NETL
NSTL
万方数据
