Statistical Modeling for Anomaly Detection, Forecasting and Root Cause Analysis of Energy Consumption for a Portfolio of Buildings

This paper describes the statistical analytics technology being developed to help K-12 public schools in New York City reduce the energy consumption. A multi-step statistical analysis procedure is proposed, to assess energy consumption and to identify energy saving opportunities for large portfolios of buildings such as the NYC K-12 public school buildings. The method borrows strength from and makes integrated use of the Variable Base Degree Day (VBDD) regression model, multivariate regression model and the Auto Regressive Integrated Moving Average (ARIMA) model. In the first step, we build a regression model which correlates the energy consumption with building characteristics for the whole portfolio of buildings. The energy related building characteristics are then identified through the stepwise variable selection technique. The results are valuable in providing building energy performance scores for the whole portfolio and benchmarking. Additionally, it offers insights for the energy consumption level of new buildings. In the second step, to accommodate building heterogeneity, we build the VBDD regression models separately for each building in the portfolio. These models are used to separate the base load energy consumption from the weather dependent usage. The results in this step consist of the base temperature estimates, as well as the estimated coefficients for the weather dependent variables, i.e., Heating Degree Days (HDD) and Cooling Degree Days (CDD) for all buildings. In the third step, we further conduct root cause analysis, by building the multivariate regression models for the base load and coefficient for HDD and CDD resulting from VBDD model, from which the performance scores can be derived for base load, heating, and cooling. Finally, in the last step, we model the dependent error structure through the ARIMA model. We also include seasonal factors in the model. The analytical method provides useful information to track and forecast the energy consumptions of the building portfolio, which will help facility staff and property managers achieve significant energy savings, greenhouse gas emission reductions and cost savings.