Adaptive interactions between building occupants and their surrounding environments affect both energy use and environmental quality, as demonstrated by a large body of modeling research that quantifies the impacts of occupant behavior on building operations. Yet, available occupant field data are insufficient to explore the mechanisms that drive this interaction. This paper introduces data from a one year study of 24 U.S. office occupants that recorded a comprehensive set of possible exogenous and endogenous drivers of personal comfort and behavior over time. The longitudinal data collection protocol merges individual thermal comfort, preference, and behavior information from online daily surveys with datalogger readings of occupants' local thermal environments and control states, yielding 2503 survey responses alongside tens of thousands of concurrent behavior and environment measurements. These data have been used to uncover links between the built environment, personal variables, and adaptive actions, and the data contribute to international research collaborations focused on understanding the human-building interaction.

The building sector consumes 75% of US electricity, offering substantial energy, cost, and CO2 emissions savings potential. New technologies enable buildings to flexibly manage electric loads across different times of day and season in support of a low-cost, low-carbon electric grid. Assessing the value of such technologies requires an understanding of building electric load variability at a higher temporal resolution than is demonstrated in previous studies of US building efficiency potential. We adapt Scout, an open-access model of US building energy use, to characterize sub-annual variations in baseline building electricity use, costs, and emissions at the national scale. We apply this baseline in time-sensitive analyses of the energy, cost, and CO2 emissions savings potential of various degrees of energy efficiency and flexibility, finding that efficiency continues to have strong value in a time-sensitive assessment framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.

The expansion of commercial building demand response (DR) as a demand-side management resource for the electric grid necessitates new decision support resources for customers to rapidly assess the benefit-risk tradeoffs of candidate load flexibility strategies. This work develops surrogate models of load flexibility impacts on office building electricity demand and indoor temperature. The surrogate models are fit to a large synthetic database generated via whole building simulations of multiple flexibility strategies under a variety of conditions; the models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. The strong predictive performance of the models underscores their potential utility in guiding DR decision-making in office settings.

The “human dimensions” of energy use in buildings refer to the energy-related behaviors of key stakeholders that affect energy use over the building life cycle. Stakeholders include building designers, operators, managers, engineers, occupants, industry, vendors, and policymakers, who directly or indirectly influence the acts of designing, constructing, living, operating, managing, and regulating the built environments, from individual building up to the urban scale. Among factors driving high-performance buildings, human dimensions play a role that is as significant as that of technological advances. However, this factor is not well understood, and, as a result, human dimensions are often ignored or simplified by stakeholders. This paper presents a review of the literature on human dimensions of building energy use to assess the state-of-the-art in this topic area. The paper highlights research needs for fully integrating human dimensions into the building design and operation processes with the goal of reducing energy use in buildings while enhancing occupant comfort and productivity. This research focuses on identifying key needs for each stakeholder involved in a building's life cycle and takes an interdisciplinary focus that spans the fields of architecture and engineering design, sociology, data science, energy policy, codes, and standards to provide targeted insights. Greater understanding of the human dimensions of energy use has several potential benefits including reductions in operating cost for building owners; enhanced comfort conditions and productivity for building occupants; more effective building energy management and automation systems for building operators and energy managers; and the integration of more accurate control logic into the next generation of human-in-the-loop technologies. The review concludes by summarizing recommendations for policy makers and industry stakeholders for developing codes, standards, and technologies that can leverage the human dimensions of energy use to reliably predict and achieve energy use reductions in the residential and commercial buildings sectors.

Buildings consume more than one-third of the world’s primary energy. Reducing energy use and greenhouse-gas emissions in the buildings sector through energy conservation and efficiency improvements constitutes a key strategy for achieving global energy and environmental goals. Building performance simulation has been increasingly used as a tool for designing, operating and retrofitting buildings to save energy and utility costs. However, opportunities remain for researchers, software developers, practitioners and policymakers to maximize the value of building performance simulation in the design and operation of low energy buildings and communities that leverage interdisciplinary approaches to integrate humans, buildings, and the power grid at a large scale. This paper presents ten challenges that highlight some of the most important issues in building performance simulation, covering the full building life cycle and a wide range of modeling scales. The formulation and discussion of each challenge aims to provide insights into the state-of-the-art and future research opportunities for each topic, and to inspire new questions from young researchers in this field.

The Oakland EcoBlock is a whole-systems urban sustainability project that aims to prototype a pathway to affordable urban decarbonization by means of block-scale retrofitting that can reduce more than 50% of home energy usage. Considering the behavioral dimension of the energy retrofit process is key to understanding how household decision-making can enhance the adoption and performance of building efficiency measures – particularly in the EcoBlock, where low-to-moderate income constraints may limit accessibility to energy efficient upgrades. This study uses two survey instruments to explore key drivers of energy efficient technology adoption and operational behavior amongst EcoBlock residents.

This study develops current and projected performance, cost, and lifetime characteristics for residential and commercial building technologies and equipment with the potential to provide grid services. The list of technologies was developed based on data gathered from the U.S. Department of Energy (DOE) Grid Interactive Efficient Buildings (GEB) Technical Report Series, ENERGY STAR Connected Certified Products, and input from researchers at the U.S. national laboratories. For each technology, characteristics are provided for a typical case and a connected or grid-interactive case. Where data are available, current DOE appliance standard levels are given. Definitions vary by technology and are provided with each data table. Current data are provided for 2020 and projections are available for 2030, 2040, and 2050.

Buildings are responsible for 36% of CO2 emissions in the United States and will thus be integral to climate change mitigation; yet, no studies have comprehensively assessed the potential long-term CO2 emissions reductions from the U.S. buildings sector against national goals in a way that can be regularly updated in the future. We use Scout, a reproducible and granular model of U.S. building energy use, to investigate the potential for the U.S. buildings sector to reduce CO2 emissions 80% by 2050, consistent with the U.S. Mid-Century Strategy. We find that a combination of aggressive efficiency measures, electrification, and high renewable energy penetration can reduce CO2 emissions by 72%–78% relative to 2005 levels, just short of the target. Results are sufficiently disaggregated by technology and end use to inform targeted building energy policy approaches and establish a foundation for continual reassessment of technology development pathways that drive significant long-term emissions reductions.

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

Full decarbonization of the U.S. energy system to meet climate goals requires aggressive emissions reductions across energy end uses; however, prevailing policies focus on end-use electrification and clean energy supply rather than a broader set of demand-side solutions. We assess multiple CO2 emissions reduction pathways to mid-century for U.S. buildings, which are among the largest sources of CO2 emissions across end-use sectors. We find potential for up to a 91% reduction in building CO2 emissions from 2005 levels by 2050 using a portfolio of efficiency, load flexibility, and electrification alongside rapid grid decarbonization. Demand-side measures could account for nearly half of overall emissions reductions, with building efficiency delivering more than double the emissions reductions of electrification measures in the near term. Further, building efficiency and flexibility would generate up to $122 billion in annual power system cost savings by 2050, offsetting nearly half the incremental cost of full grid decarbonization. Presented on April 25, 2022.