Modeling of Building Energy Consumption Prediction Based on Graph Neural Networks
In urban building management,the high proportion of building energy consumption is a huge problem at present.Accurately predicting building energy consumption is of great significance to achieve building energy conservation and the building of smart cities.Due to the complexity of energy consumption data,long-term and accurate forecasting of building energy consumption is one of the most challenging problems in time series forecasting.In recent years,researchers have applied neural network models to the task of energy consumption prediction and achieved excellent prediction results.However,building energy consumption is affected by multidimensional factors.In order to improve the prediction accuracy,this paper proposes the modeling of building energy consumption prediction based on graph neural networks.The method uses a modified graph convolutional network to capture the spatial dependencies of time series,and a temporal convolution module to obtain the temporal dependencies of time series.Through the fusion of time and space,time series features that multivariate time series can be more fully mined,and joint learning in an end-to-end framework can be supported.The experimental results on the real energy consumption dataset confirm that the model has better performance.
building energy consumptionbuilding energy efficiencygraph neural networkenergy consumption fore-castspatial dependencetiming characteristics
NETL
NSTL
万方数据
