MULTI-FEATURE RECURRENT NEURAL NETWORK FOR BUILDING COOLING LOAD SOFT SENSING
Real-time control on HVAC system,of great significance to building conservation,is based on accurate cooling load real-time calculation,which can be realized by soft sensing.In order to improve the prediction performance of soft sensing model,a multi-feature recurrent neural network based on Stacking algorithm is proposed,which integrates five machine learning algorithms.In order to verify the prediction performance of the model,comparative experiments were carried out on three building energy simulation data sets from different types of buildings.The experimental results show that the prediction performance of the proposed model is greatly improved compared with the base models,and the MAE and RMSE are the best compared with other integrated models and time series forecasting models,and have good prediction stability.
万方数据
维普
