Research in occupant behaviour is now using a more elaborate framework of building occupant interaction. Researchers often face challenges in collecting data, particularly for the data to meet the minimum number of required data points and the data interoperability requirements. Researchers address the first issue with the synthetic population and the latter with data ontologies. While synthetic population is commonly used to address the first issue, data ontology development is used to address the latter. The two solutions are complementary to each other. One of the known ontologies in building occupant behaviour research is the Drivers-Needs-Actions-Systems (DNAS) ontology, which has been used by building modelers to describe energy-related occupant behaviour. This paper describes the ontology-based synthetic population generation that can be used in the agent-based modeling (ABM) applications. This paper considers multiple data sources, including ASHRAE Thermal Comfort DB II and IEA Annex 66 data sets. A case study of an office building is used to present the workflow of DNAS framework expansion, synthetic population generation, and agent-based modeling.

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 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.

Buildings are energy-intensive and a primary source of US end-use sector carbon emissions. Although building emissions today are 25% below their 2005 peak, far deeper reductions are needed to reach the US 2050 net-zero emissions goal. However, plausible decarbonization pathways that consider both buildings and their interactions with the power grid remain poorly understood. Here, we couple detailed modeling of building energy use and the grid to quantify building decarbonization potential and associated grid impacts. We find up to a 91% reduction in building CO2 emissions from 2005 levels by 2050 using a portfolio of building efficiency, demand flexibility, and electrification measures alongside rapid grid decarbonization. Building efficiency and flexibility could generate up to $107 billion in annual power system cost savings by 2050, offsetting over a third of the incremental cost of full grid decarbonization. Our results underscore multiple benefits of demand-side solutions for deep decarbonization of US buildings.

Buildings use 75% of US electricity; therefore, improving the efficiency and flexibility of building operations could provide significant value to the rapidly changing electricity system. Here, we estimate the technical potential near- and long-term impacts of best-available building efficiency and flexibility measures on annual electricity use and hourly demand across the contiguous United States. Co-deployment of building efficiency and flexibility avoids up to 742 TWh of annual electricity use and 181 GW of daily net peak load in 2030, rising to 800 TWh and 208 GW by 2050; at least 59 GW and 69 GW of the peak reductions are dispatchable. Implementing efficiency measures alongside flexibility measures reduces the potential for off-peak load increases, underscoring limitations on load shifting in efficient buildings. Overall, however, we find a substantial building-grid resource that could reduce future fossil-fired generation needs while also reducing dependence on energy storage with increasing variable renewable energy penetration.