TA-23-C081 – An Innovative Building Energy Use Analysis by Unsupervised Classification and Supervised Regression Models ✓ Most Recent

The traditional building energy use analysis classification is typically conducted according to the primary uses. However, some buildings with different primary uses have similar energy use patterns. This is because multiple attributes, other than building types, impact energy. Also, the same building type may have significantly different energy use patterns due to factors such as building age, size, equipment, and climate conditions. In order to better improve the performance of building energy benchmarking, this study employs the unsupervised K-Means++ model to classify the building energy benchmarking with comprehensive weather data from four cities in ASHRAE climate zones 3A, 4A, 5A, and 6A under five years from 2016 to 2020. During the classification process, the Elbow and Calinski Harabasz metrics are conducted to optimize the number of clusters. After classifying the total dataset, this study splits the data of each cluster into training and test sub-datasets for training and testing. Each test dataset is isolated by the same year in each climate zone. Then, LGBM tree-boosting ensemble regression model is applied to evaluate the model performance for each cluster. During the training process of each cluster, the Grid-search with Cross-validation is operated to optimize the hyperparameters and improve the running efficiency. The results show that each classified model by K-Means++ performs well, all achieving more than 80% R2s for actual and predicted values. Furthermore, the building clusters are significantly different from the primary use classification. In addition, the LGBM model features positive and negative impacts on the building energy use intensity of each cluster is also investigated, giving a deeper scope in understanding the model performance for future studies.