Research On The Methods And Application Of Cooling And Heating Load Short-term And Ultra-short-term Prediction For The Office Buildings

At present,the building HVAC system has a very large energy consumption.The accurate prediction of the cooling and heating load can help the building managers to know the energy demand in advance and improve the operation efficiency.Based on the measured data of an office building in Tianjin,the study presents a method for establishing building load prediction model,and mainly focus on the model inputs optimization.And then,the study reflects the energy saving effect of building load prediction.Two type of prediction models,which include short-term prediction and ultra-short-term prediction,are analyzed.For the short-term prediction model,this paper uses correlation analysis to select the influence factors preliminarily based on the real-time exterior variables firstly.Considering the thermal inertia of the building envelope,the influences of historic hours'exterior variables are also analyzed.Then,the principal component analysis is used to process the influence factors and the model inputs are obtained.Finally,the artificial neural network and support vector regression prediction models are established using model inputs,respectively.For the ultra-short-term prediction model,the interior variables will also be analyzed.Before selecting the influence factors,the load signals should be processed into multi-frequency-band signals by the wavelet decomposition,and then,the models are established on different frequency bands.The predicted load can be obtained through wavelet reconstruction.Using the measured data of case building to verify the proposed modeling methods,the prediction results show that the R~2 of short-term and ultra-short-term heating load prediction model can reach 68.1%and 94.0%respectively,the R~2 of short-term and ultra-short-term cooling load prediction model are 71.3%and 83.9%respectively.Comparing the various model inputs selection methods,the results show that the superiority of the model inputs optimization method proposed in this study,which is based on wavelet decomposition and reconstruction,correlation analysis,and principal component analysis.The importance of the interior and exterior variables that influencing the building load is compared.The conclusions show that the building heating load is mainly influenced by exterior variables,and only the exterior variables processed for model inputs can obtain a high prediction accuracy;for the cooling load prediction,the influence of interior variables is more important than that of exterior variables,however,only the interior variables processed for the model inputs cannot ensure a satisfactory prediction accuracy,the exterior variables should not be ignored when modelling.This study uses load prediction models to obtain the building energy demand ahead of time,and then guide the operation adjustment,which has great energy saving effect.