A data-driven model for building energy normalization to enable eco-feedback in multi-family residential buildings with smart and connected technology

Abstract

In this paper, we present a new unit-level data-driven modelling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings based on easily accessible data from smart thermostats and WiFi-enabled power metres. Our physics-informed approach starts from a heat balance equation to derive a linear regression model and uses a Bayesian mixture model to identify groups of units that have similar regression coefficients. Our model captures the effect of behaviour on HC energy consumption by normalizing the effect of building characteristics and accounting for the inter-unit heat transfer and unobserved variables. Our probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data become available. Using yearly data collected in a multi-family building, our model identifies distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.