Research on a CNN-LSTM building energy consumption prediction method based on attention mechanism
The analysis and prediction of building energy consumption is a key technology to improve the energy efficiency of buildings and an important means to address the national"dual-carbon"strategy.Due to the strong temporal characteristics of building energy consumption data,it is difficult to effectively extract high-dimensional features in the data by using traditional deep learning techniques,and it is easy to lose important information.Therefore,this paper proposes a CNN-LSTM building energy consumption prediction method based on the attention mechanism,which uses CNN to extract spatial features in energy consumption data,LSTM to process time series data,and attention mechanism to determine feature weights,improving the prediction accuracy of the model.
building energy consumptionpredictiondeep learningconvolutional neural networklong short-term memory networkattention mechanism
万方数据
维普
