Building America Best Practices Series - High-Performance Home Technologies: Guide to Determining Climate Regions by County

Baechler, Michael C.; Gilbride, Theresa L.; Cole, Pam; Hefty, Marye G.; Ruiz, Kathi

doi:10.2172/1217886

Cited by 26 publications

(33 citation statements)

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“…The total cost during the peak hour is $7.5 billion, nearly three times higher than during 3-4 AM. Space cooling again accounts for 10 The database used for this mapping is publicly available [36] and draws from the US Census Bureau [37], the US Environmental Protection Agency's eGRID database [38], and data from the Pacific Northwest National Laboratory used for mapping climate regions to counties [39]. 11 For example: the total primary electricity use attributable to air source heat pump operation in single family homes in northern climates between 1 and 2 PM on summer weekdays in the year 2030. a substantial share of total costs, around 41% during the peak hour.…”

Section: Hourly End-use Electricity Consumption Cost and Emissions mentioning

confidence: 99%

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Satre-Meloy

Langevin

2019

Environ. Res. Lett.

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The building sector consumes 75% of US electricity, offering substantial energy, cost, and CO 2 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 CO 2 emissions savings potential of various degrees of energy efficiency and flexibility, finding that efficiency continues to have strong value in a timesensitive 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.

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Section: Hourly End-use Electricity Consumption Cost and Emissions mentioning

confidence: 99%

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Satre-Meloy

Langevin

2019

Environ. Res. Lett.

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“…The data that were acquired were skewed to buildings from California, and Washington, DC, with much less representation from other regions. Hence, the test dataset for the analyses presented in this paper comprised 537 commercial buildings from multiple ASHRAE climate zones [2], and is characterized in Table 1. For each building, 15-minute whole-building electricity data was paired with outside air temperature that was determined from the building's zip code.…”

Section: Test Datamentioning

confidence: 99%

Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings

et al. 2016

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Trustworthy savings calculations are critical to convincing investors in energy efficiency projects of the benefit and cost-effectiveness of such investments and their ability to replace or defer supply-side capital investments. However, today's methods for measurement and verification (M&V) of energy savings constitute a significant portion of the total costs of efficiency projects. They also require time-consuming manual data acquisition and often do not deliver results until years after the program period has ended. The rising availability of "smart" meters, combined with new analytical approaches to quantifying savings, has opened the door to conducting M&V more quickly and at lower cost, with comparable or improved accuracy. These meter-and software-based approaches, increasingly referred to as "M&V 2.0", are the subject of surging industry interest, particularly in the context of utility energy efficiency programs. Program administrators, evaluators, and regulators are asking how M&V 2.0 compares with more traditional methods, how proprietary software can be transparently performance tested, how these techniques can be integrated into the next generation of whole-building focused efficiency programs. This paper expands recent analyses of public-domain whole-building M&V methods, focusing on more novel M&V2.0 modeling approaches that are used in commercial technologies, as well as approaches that are documented in the literature, and/or developed by the academic building research community. We present a testing procedure and metrics to assess the performance of whole-building M&V methods. We then illustrate the test procedure by evaluating the accuracy of ten baseline energy use models, against measured data from a large dataset of 537 buildings. The results of this study show that the already available advanced interval data baseline models hold great promise for scaling the adoption of building measured savings calculations using Advanced Metering Infrastructure (AMI) data. Median coefficient of variation of the root mean squared error (CV(RMSE)) was less than 25% for every model tested when twelve months of training data were used. With even six months of training data, median CV(RMSE) for daily energy total was under 25% for all models tested. These findings can be used to build confidence in model robustness, and the readiness of these approaches for industry uptake and adoption.

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“…These three states are in dramatically different climate zones. According to the climate zones defined by the US Department of Energy Building America Program [14], Arizona is largely in the "Hot-Dry" zone, Ohio is largely in the "Cold" zone, and Minnesota is mostly in the "Cold/Very-Cold" zone. The diversity of climate zones in the USA contributes to the difficulty in establishing quantitative relationships between health indicators (like the penetration rates used here) and climate variables, given that individual states can be very different in other ways besides climate.…”

Section: Gdp % Urban and Unemployment Datamentioning

confidence: 99%

Relationship between Ambient Temperature and Mental Health in the USA

Phelan

Phelan²

2017

Environments

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Climatic variables such as temperature have been shown to correlate with demand for mental health services in other countries. An attempt by the present study to replicate this correlation using existing USA treatment data on mental health was not substantiated. Using annual state-level data from 2007 through 2015, the rate of mental health service utilization per 1000 population was correlated with average temperature and precipitation, while adjusting for Gross Domestic Product (GDP), unemployment, and urbanization. No statistically significant correlation was found.

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Building America Best Practices Series - High-Performance Home Technologies: Guide to Determining Climate Regions by County

Cited by 26 publications

References 0 publications

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings

Relationship between Ambient Temperature and Mental Health in the USA

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