Commercial buildings are responsible for 36% of electricity consumption in the United States. Interactions among engineering, organizational, and behavioral factors and incentives determine building energy use and related environmental sustainability, and potentially cause divergence between true energy performance and theoretically possible performance. There has been increasing investment in energy efficient commercial building designs and technologies. Among the studies that conduct statistical post-occupancy evaluations (POE), there has been debate about whether "green" commercial buildings (i.e. LEED buildings) actually save energy or not, and this casts doubt on their energy sustainability. Evaluating the true energy performance of green commercial buildings requires empirical analysis of long data histories and larger sample sizes. To understand a complete picture of energy sustainability, social factors such as organizational management and occupant behaviors, as well as environmental and economic impacts of energy use, must be studied. Additionally, evaluating the effects of these factors on the timing of building energy use (i.e. peak hours versus non-peak hours) is essential to understand environmental (i.e. carbon) costs of electricity consumption. This project will conduct such holistic energy sustainability analysis using a comprehensive building energy dataset in a city (Phoenix) that is typical of energy use patterns in many of the world's rapidly growing hot and dry urban areas.
Specifically, the project will: 1) provide reliable statistical evidence of the true energy performance of green commercial buildings based on large datasets; 2) quantify the impact of green commercial buildings on power grid load profiles and electricity generation fuel mix; 3) apply surveys to quantify engineering, organizational, and behavioral factors that determine observed building energy performance; and 4) evaluate the systemic environmental and economic impacts of green commercial buildings in the Phoenix metropolitan area. The project will advance engineering research into energy sustainability of green commercial buildings through a novel "big data" empirical approach. First, this project will analyze much larger and reliable datasets of commercial buildings than previously studied, including historical electricity consumption and other key building level attributes. The dataset contains more than 700 Energy Star and LEED commercial buildings, and more than 17,000 total commercial electric customers. Second, compared to existing POE studies, this project adopts more rigorous and advanced statistical analysis including matching, panel regression, and nonparametric modelling. Importantly, this project uses much more credible control groups. Third, this project will provide the first empirically reliable evidence of how green building technologies impact environmental footprints by time of day and season, which are critical for determining environmental costs associated with electricity consumption. Finally, this project will empirically explore the interrelated factors in the coupled human-environmental systems including engineering (e.g. technical reliability and compatibility), organizational (e.g. principal-agent problem), and behavioral (e.g. bounded rationality) factors that influence the observed energy performance of commercial buildings and the impact on environmental sustainability. The insights produced may be directly implemented by practitioners including state energy policy makers, city environment planners, energy service companies, building engineers, building owners, and utility companies.
National Science Foundation, Division of Chemical, Bioengineering, Environmental, and Transport Systems