MEBA: AI-powered precise building monthly energy benchmarking approach

Monthly energy benchmarking supports identifying trends, improving energy efficiency, and conducting cost management for building owners, managers, and policymakers better than annual or hourly benchmarking. Annual data cannot fully reflect operation utility status, and hourly data poses the issue of high-cost data mining and incomparability due to its minor scale. However, the primary challenges of monthly energy benchmarking are data limitation, "black-box" barrier, and building classification uncertainty. This study proposes a novel AIpowered Monthly Energy Benchmarking Approach (MEBA) to better assess building energy use patterns, benchmark end-use loads, and track utility bills. MEBA addresses two scenarios: (1) predict complete year-round monthly energy using partial monthly energy data; (2) estimate monthly energy loads from annual total energy data. The study collects monthly electricity and natural gas energy use from two U.S. cities. For the first scenario, the entire dataset is clustered into two primary groups by Gaussian Mixture Model (GMM). Then, the two groups are divided by Self-Organizing Map (SOM) models into five subclusters via energy use patterns. For the second scenario, an additional step is needed to locate the subcluster labels with advanced Light Gradient Boosting Machine (LGBM) classifications. All five subclusters have high prediction performance with an average accuracy of >95%. Both scenarios require the last stage to predict monthly electricity and natural gas by LGBM regressions. MEBA's prediction performance achieves R(2)s ranging from 0.50 to 0.73, with RMSEs between 0.15 and 2.35, outperforming the state-of-the-art XGBoost model. Each subcluster exhibits distinct energy use patterns, with EUIs, electricity loads, and year built as the most significant attributes.