Estimating electricity impact profiles for building characteristics using smart meter data and mixed models

Abstract

Understanding the impact of building characteristics on electricity demand is important for policy and management decision making. Certain building characteristics and equipment may increase or decrease electricity consumption. Due to different operating practices, these impacts on electricity consumption may vary both across the day and across seasons. Quantifying the magnitude and statistical significance of these impacts will help managers and policy makers make better informed decisions. Here we present a mixed effects model to assess the importance of several variables on building electricity consumption. We use smart meter and building attribute data for 129 commercial office buildings. Our building attribute data includes information on installed equipment and meter characteristics of each building. To account for uncertainty in both variable significance and model selection we follow a multimodel inference approach. Demand impact profiles that show the expected change in electricity demand when a characteristic is absent or present are produced for each season. A discussion of the commercial office building characteristics we use and their impact on the daily profile of electricity demand is presented. Our approach has the advantage of only requiring building level demand and characteristic data. No equipment level sub-metering is required. Furthermore, our approach can also be used to quantify changes in electricity consumption caused by other factors that do not directly draw electricity from the grid, such as management decisions or occupant behaviour. We conclude with a discussion of applications for our methodology and future research directions.