Prediction of building energy consumption based on advanced feature mining and Bayesian MCMC method

To solve the problems of difficult selection of explanatory variables, limited building sample data and difficult capturing of complex nonlinear relationships in modeling algorithms, an energy consumption prediction model based on advanced feature mining and Bayesian MCMC method is proposed. In this study, the characteristic indicators such as building ontology, building environment and occupancy behavior are used as model inputs to identify high-response interaction factor combinations, and the heating energy consumption data from 205 office buildings in the CBECS database are used as training set samples, and the combination of high-response interaction factors is identified by symbol test and K-fold cross-validation, and the importance sampling is carried out by the MCMC method to predict the heating energy consumption intensity of office buildings. It is found that there is a high-order interaction between the floor height, area and number of floors of the building, and the normalized mean biased error (NMBE) of the model prediction accuracy index is 3.5%, the root mean square error (RMSE) is 0.014, and the coefficient of variation of the root mean squared error (CVRMSE) is 14.0%, which can effectively predict heating energy consumption of office buildings.