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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. • A range of commercial load flexibility strategies are developed as OpenStudio Measures. • Synthetic data of Measure impacts are compiled across building types, vintages, and climates. • Models fit to synthetic data predict changes in demand and temperature under load flexibility. • A Bayesian framework facilitates model updating and communication of prediction uncertainty. • Acceptable model fit and predictive performance is demonstrated across multiple metrics. [ABSTRACT FROM AUTHOR]

Abstract: Low-income housing constitutes an important but often overlooked area for energy use reductions within the US residential sector. Given the scarcity of existing information on this subject, this study uses a semi-structured interview format to explore the key behavioral tendencies, energy knowledge gaps, and attitudes among low-income public housing residents, with the goal of demonstrating a process for developing, scoring, and analyzing the interviews that will be useful to other researchers when first engaging complex subjects like behavior in contexts that are not well covered by existing literature. Methods for sampling subjects and iteratively developing an interview guide and response-scoring framework are described, and the usefulness of this approach in allowing both quantitative and qualitative analysis of behavior response data is demonstrated. The paper concludes by illustrating how themes that emerge from the response analysis can be used to inform future surveying and intervention efforts. Key themes include the varying definitions for “comfort” amongst residents; the lack of resident control over the household environment; the tendency for residents to evaluate energy conservation measures (ECMs) in terms of costs, savings, and comfort; the muted differences in behavior between those who do and do not pay energy bills; and the importance of building maintenance and resident energy education to ongoing efficiency efforts. [Copyright &y& Elsevier]