Statistical modeling for anomaly detection, forecasting and root cause analysis of energy consumption for a portfolio of buildings

Abstract

A multi-step statistical analysis procedure is developed to assess energy consumption and to identify energy saving opportunities for large portfolios of buildings such as the New York City's 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. The analytical method provides useful information to compute energy performance metrics, detect anomaly, forecast and analyze root causes of the energy consumptions of the buildings, and helps building facility engineers and property managers to achieve significant energy savings, greenhouse gas emission reductions and cost savings.