Research on Building Energy Consumption Decision Prediction Method Based on GMM

Building energy prediction plays an important role in building energy management, energy conservation and fault diagnosis. Aiming at the difficulty that the features of building energy consumption data sets are nonlinearly related and there are outliers, the FGLnet building energy consumption integrated prediction model for building energy consumption based on feature extraction, clustering, and Light Gradient Boosting Machine is proposed. Firstly, the mutual information method and recursive feature elimination are used to make secondary feature selections on the original building dataset to remove redundant features. Then the data with similar features are reasonably clustered based on Gaussian Mixture Model algorithm. The several Light Gradient Boosting Machine with different parameters are used to train and predict the energy consumption data of each cluster separately. Through comparison experiments, it can be seen that the integrated model can effectively improve prediction accuracy and convergence speed.