Data-driven, multi-metric, and time-varying (DMT) building energy Benchmarking using smart meter data

Abstract

New and emerging data streams, from public databases to smart meter infrastructure, contain valuable information that presents an opportunity to develop more robust data-driven models for benchmarking energy use in buildings. In this paper, we propose a new Data-driven, Multi-metric, and Time-varying (DMT) energy benchmarking framework that utilizes these new data streams to benchmark building energy use across multiple metrics at the daily time scale. High fidelity data from smart meters enables the DMT benchmarking framework to produce daily benchmarking scores and use daily weather data to understand seasonally adjusted performance. Intra-day building efficiency is also investigated by benchmarking buildings across several metrics (e.g., total energy usage, operational energy usage, non-operational energy usage) thereby enabling deeper insights into building operations than traditional yearly benchmarking models. By using quantile regression modeling, the DMT framework can differentiate and understand the main drivers of energy consumption between low and high performing buildings and between building operational states. To illustrate the insights that can be gleaned from the proposed DMT framework, we apply the framework to understand building performance for over 500 schools throughout the state of California. The DMT framework provided insights into how various drivers impacted energy usage for both high and low performing buildings, and results indicated that schools had consistent drivers of energy usage. Overall the DMT framework was designed to be highly interpretable such that it could help bridge the gap between data science and engineering methods thus enabling better decision-making in respect to energy efficiency.