Simulation of Green Building Energy Consumption Prediction Method Based on Channel Singular Spectrum Analysis
Generally,building energy consumption is influenced by the external environment,user behavior,and other factors.However,these factors are difficult to predict accurately or update at a low frequency,resulting in uncer-tainty in energy consumption prediction.In order to achieve highly accurate energy consumption prediction for green buildings,this article presented a Bayesian method for predicting the energy consumption of green buildings.Firstly,Multi-channel Singular Spectrum Analysis(MSSA)was employed to denoise historical data about the energy con-sumption of green buildings.And then,these data were used as the training samples.Moreover,target localization and search were conducted using the training samples.Meanwhile,feature extraction was performed on the obtained targets.Furthermore,a Bayesian prediction model of energy consumption was built based on the correlation between target features and target identities.Finally,the target prediction results were output through relevant decision condi-tions,thus completing the energy consumption prediction of green buildings.Simulation results show that the proposed method can achieve highly accurate energy consumption prediction and has good data denoising capability.
Green buildingsEnergy consumption predictionBayesianMultichannel singular spectrum analysis
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万方数据
