To address the data quality and the input parameters screening of the HVAC system energy consumption prediction,this research investigates the potential parameter optimization approach of the HVAC energy consumption prediction model based on the Transformer neural grid.The result indicates that a Generalized Extreme Studentized Deviate(GESD)algorithm can be used to find outliers,correct data,and thus improve data quality.The initial screening of the input parameters is conducted through a cosine similarity association test,which will reduce the multicollinearity between the parameters.To review and screen the final input feature parameters,the accuracy of the prediction results is determined using the Random Forest(RF)algorithm,which helps to eliminate extraneous elements.The final energy consumption prediction model of HVAC system shows good prediction performance on test dataset,with an R2of 0.952 for the test set of data and a root mean square error(RMSE)of 38.831 kW.
energy consumption prediction of HVAC systemTransformer neural networksdata qualitymodel parameter optimization
NETL
NSTL
万方数据
