EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Traditionally, specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and household demographics. For the 2020 survey cycle, EIA used Web and mail forms to collect detailed information on household energy characteristics. This information is combined with data from energy suppliers to these homes to estimate energy costs and usage for heating, cooling, appliances and other end uses — information critical to meeting future energy demand and improving efficiency and building design. Archived from https://www.eia.gov/consumption/residential/

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into "https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

• The PUDL Repository on GitHub
• PUDL Documentation
• Other Catalyst Cooperative data archives

The 2015 study represents the 14th iteration of the RECS program. First conducted in 1978, the Residential Energy Consumption Survey is a national sample survey that collects energy-related data for housing units occupied as a primary residence and the households that live in them. Data were collected from more than 5,600 households selected at random using a complex multistage, area-probability sample design. The sample represents 118.2 million U.S. households. The 1st version of the 2015 RECS microdata file, released in April 2017, reflected preliminary household characteristics data. The file was updated in October 2017 (Version 2) to include additional square footage and household energy insecurity data. The file was updated again in May 2018 (Version 3) and included final household characteristics data, as well as final consumption and expenditures data. The final version of the microdata file was updated in December 2018 (Version 4) and contains wood consumption variables, as well as additional weather and climate-related variables used in the end-use modeling process.

We periodically conduct the Residential Energy Consumption Survey (RECS), which provides detailed estimates of energy usage in U.S. homes. This dashboard includes selected residential energy site consumption, expenditures, and household characteristics information from the 2020 RECS, the 15th iteration of the survey. You can find additional information about the 2020 RECS, including terminology, methodology, microdata file, and data tables, on our website. Estimates and relative standard error values are available in the state data tables. Perceived differences between the estimates displayed in the bar chart may not be statistically significant. Notes: Some estimates are not available for all states (Data withheld) because either the relative standard error (RSE) was greater than 50% or there were fewer than 10 households in the reporting sample. These are identified as “Q” or “N” in the RECS data tables. Exercise caution when deriving withheld estimates from published estimates. Data source: U.S. Energy Information Administration, Office of Energy Demand and Integrated Statistics, 2020 Residential Energy Consumption Survey

Abstract

This is a state-level summary of the Residential Energy Consumption Survey (RECS) 2020, for 50 states and the District of Columbia.

The survey measures characteristics that contribute to energy consumption in U.S. households.

The summarized datasets cover the following topics:

• Space heating
• Air conditioning
• Water heating
• Household characteristics
• Energy assistance

Methodology

• **Target population: **all a) occupied housing units in b) the 50 states & DC that are used as c) primary residences.
• Sample: Homes that are occupied as a primary residence
• Excludes vacant homes, seasonal housing units, group quarters (e.g. dormitories, nursing homes, prisons, military barracks), and common areas in apartment buildings.

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• Includes housing units located on military installations.
• **Sampling method: **Unclustered sampling, Jackknife method
• **Mode of survey: **self-administered questionnarie via 1) the web or 2) mail/paper
• **Sample size: **18,496 households (72% via web questionnaire; 27.2% via paper questionnaire)
• Response rate: Unweighted, 38.6%; Weighted, 37.9%

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Usage

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• The summarized datasets are weighted estimates of the number of households in each state.

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• Users should exercise caution in using estimates based on samples, as sample sizes could be small and the standard errors could be large.
• RECS is best suited for comparison across different characteristics of homes within the residential sector.
• **RECS is **%3Cu%3E%3Cstrong%3Enot%3C/strong%3E%3C/u%3E

** appropriate for comparing EIA's other residential energy data** as the scope of RECS is limited to homes occupied as a primary residence. As a result, RECS estimates are not comparable with sector-level totals defined in other EIA datasets

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Abstract

Dataset quality **: Medium/high quality dataset, not quality checked or modified by the EIDC team

RECS measures the usage of energy in primary, occupied housing units, in 2020. This is the raw dataset measured at the household level.

It covers the following topics:

• the physical characteristics of the home (housing types, size, and age);
• the use of electronic devices and appliances;
• the use of heating and air conditioning systems;
• energy usage behaviors (frequency of usage, temperature setpoints);
• challenges associated with paying utility bills or keeping homes at healthy temperatures;
• total household energy consumption and expenditures; and
• their breakdown by energy end-use (modeled/estimated), such as by space heating, water heating, air conditioning, cooking, laundry, TV, lighting, and other end-uses.

Methodology

• **Target population: **all a) occupied housing units in b) the 50 states & DC that are used as c) primary residences.
• Sample: Homes that are occupied as a primary residence
• Excludes vacant homes, seasonal housing units, group quarters (e.g. dormitories, nursing homes, prisons, military barracks), and common areas in apartment buildings.

%3C!-- --%3E

• Includes housing units located on military installations.
• **Sampling method: **Unclustered sampling, Jackknife method
• **Mode of survey: **self-administered questionnarie via 1) the web or 2) mail/paper
• **Sample size: **18,496 households (72% via web questionnaire; 27.2% via paper questionnaire)
• Response rate: Unweighted, 38.6%; Weighted, 37.9%

%3C!-- --%3E

Usage

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• RECS is best suited for comparison across different characteristics of homes within the residential sector.

%3C!-- --%3E

• **RECS is **%3Cu%3E%3Cstrong%3Enot%3C/strong%3E%3C/u%3E

** appropriate for comparing EIA's other residential energy data** as the scope of RECS is limited to homes occupied as a primary residence. As a result, RECS estimates are not comparable with sector-level totals defined in other EIA datasets

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The Building Energy Data Book (2011) is a compendium of data from a variety of data sets and includes statistics on residential and commercial building energy consumption. Data tables contain statistics related to construction, building technologies, energy consumption, and building characteristics. The Building Technologies Office (BTO) within the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy developed this resource to provide a comprehensive set of buildings- and energy-related data.

The Data Book has not been updated since 2011.

The data sets comprising the Data Book are now publicly available in user-friendly formats and you can use them to find data relevant to your questions. Please find below a list of Energy Information Administration (EIA) data sets that BTO consults:

• Annual Energy Outlook

  Projections in the Annual Energy Outlook 2015 (AEO2015) focus on the factors expected to shape U.S. energy markets through 2040.

• Monthly Energy Review

  A publication of recent and historical energy statistics.

• Residential Energy Consumption Survey (RECS)

  A nationally representative sample of housing units that specially trained interviewers collect energy characteristics, usage patterns, and household demographics.

• Commercial Buildings Energy Consumption Survey (CBECS)

  A national sample survey that collects information on the stock of U.S. commercial buildings, including their energy-related building characteristics and energy usage data.

Questions about the above resources can be directed to the relevant EIA subject matter expert.

This dataset contains hourly load profile data for 16 commercial building types (based off the DOE commercial reference building models) and residential buildings (based off the Building America House Simulation Protocols). This dataset also uses the Residential Energy Consumption Survey (RECS) for statistical references of building types by location. Hourly load profiles are available for over all TMY3 locations in the United States here.

Browse files in this dataset, accessible as individual files and as commercial and residential downloadable ZIP files. This dataset is approximately 4.8GiB compressed or 19GiB uncompressed.

July 2nd, 2013 update: Residential High and Low load files have been updated from 366 days in a year for leap years to the more general 365 days in a normal year.

Recent data from the United States (US) Energy Information Administration reveals that nearly one in three households in the US report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We produce a novel machine learning approach based on sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. Our analysis confirms that average household energy burdens increased, and the range of households suffering energy poverty broadened. We provide an optimized allocation structure to urge policy makers to revise the distribution of funds to better match assistance needs. Methods We use machine learning to determine how various demographic and physical characteristics are correlated with household energy burdens across the US. Energy burden estimates allow us to identify where energy poverty may be concentrated at the census-tract level. Our analysis extends and improves upon the Low-income Energy Affordability Data (LEAD) tool, developed by the US Department of Energy’s National Renewable Energy Laboratory to estimate energy expenditures and burdens in several ways (28). The LEAD tool is designed to help local and state governments with decisions for addressing energy poverty; however, it is static in time and uses self-reported energy expenditures given only for one month of the year, which is not reported publicly. The reliance on one month implies that the estimation of annual values is not guaranteed to account for the seasonal variation in energy costs throughout the months. The sampling done by the survey must sufficiently cover all months of the year, and this is not verifiable from the publicly available data. In addition, which month is used varies across respondents. Different from LEAD, we use household-level sociodemographic and geographic data, detailed in the following subsection, from the Energy Information Administration’s (EIA) Residential Energy Consumption Survey (RECS) to estimate the annual energy burden. This survey is completed every five years, enabling us to track changes in energy burden over time. To develop our projections at a census-tract level, we use an adaptive least absolute shrinkage and selection operator (LASSO) technique to select important variables from the RECS data to be applied to census-tract level information from the US Census Bureau’s American Community Survey (ACS).

If we examine the EIA process in different countries, it seems that it is linked to a previous phase of discussion of projects, through the administrative process.This previous stage is, in fact, one in which the project can be publicly discussed and the decisions about location, design, etc., are made, and one in which the preventive focus of the EIA can be applied. In consequence, the EIA process looks as if it were linked to the pre-construction period, although, in fact, it must be a cyclic process, with some control instruments to guarantee the performance of mitigation measures and auditing effect forecasts. However, of central interest in determining the effectiveness of the EIA is the extent to which the environment is managed and protected as a result of the whole EIA process. Environmental auditing is an important tool for providing an account of construction and post-development (EIA) activities. In this paper, we analyze the experience in auditing programs of project and construction of highway projects in Spain, in the last ten years, concluding the importance of auditing programs to guarantee the success of the EIA process, and establishing the basis of its contents, structure and implementation, particulary in the field of ecological impacts. The paper discusses the content and structure of auditing programs, the agents involved in the process, and the responsibilities of each one looking at the experience in Spain. This analysis permits, also, identify the frequent practice in preventive, corrective and compensatory measures in the highway projects in Spain. This paper reflects, in part, the conclusions derived of a research project financed, during 2002, by the Centro de Estudios y Experimentación de Obras Públicas (CEDEX, Research Centre of Public Works) of the Spanish Ministry of Public Works (Ministerio de Fomento), througth the analysis of more than 40 highway projects developed in the last ten years in Spain, most of them already constructed. Moreover, it reflects, also, the results of the works related with the Quality Verification of Environmental Annexes made by the authors for ESTEYCO, technical assistance during several years of the Spanish Ministry of Public Works for the Quality of Projects Assurance Plan.

The Office of Policy Development and Research (PD&R) has developed the HUD Utility Schedule Model to provide a consistent basis for calculating utility schedules. The current HUSM is a web application that uses correlations and regression techniques to calculate allowances for end-uses, as specified on form HUD-52667 (Allowances for Tenant-Furnished Utilities and Other Services). This version of the model is primarily based on the 2009 Residential Energy Consumption Survey1 (RECS) dataset that is published by the Energy Information Administration (EIA) of the Department of Energy (DOE). Updates to this version of the model include: “floor” and “ceiling” values for all utilities types;providing users the ability to generate allowance estimates based on zip code, in addition to PHA;updating the underlying degree-day data with the latest NOAA 30-year weather data (1981-2010);updates to the water usage estimates based on U.S. Geological data;incorporating additional green discounts (i.e., LEED and Significant Green Retrofits);refining the model’s heating consumption estimates;incorporating a factor adjustment feature;updating the list of Section 8 PHAs.

In cooperation with the environmental lawyers of the provinces of Carinthia & Lower Austria

Editors: I like it. Matthias Schmidt, DI Manuel Denner, Dr. Michael Dvorak, Johannes Hohenegger, Dr. Remo Probst, MSc. Christina Nagl, Dr. Erwin Nemeth, MMag Bernadette Strohmaier, Dr. Gábor Wichmann

Citation suggestion: BirdLife Austria (2021): Guide for ornithological surveys in the context of nature conservation and EIA procedures for the approval of wind turbines and distance recommendations for wind turbines to breeding sites of selected bird species. Guide in cooperation with the environmental lawyers of the provinces of Carinthia & Lower Austria. BirdLife Austria, Vienna, 40 pp.

The new millennium witnessed an unanticipated escalation in the installation of rooftop solar panels, particularly due to the development of highly efficient power electronic converters (PECs). The ‘battle of currents’ between AC and DC, which settled in the favor of AC in the nineteenth century, reignited as DC is striking back due to this technological augmentation. The shifting trend towards DC is more pronounced in the residential sector, which necessitates a comparative analysis of AC and DC at distribution scale on realistic grounds. Modern home data extracted from the energy information administration (EIA) has been utilized to devise a mathematical model based on bottom-up approach. The comparative analysis has been performed encompassing scenarios of varying PEC efficiencies as a result of daily load variation. Moreover, the scenarios of multiple PEC efficiencies and rooftop solar capacities are also considered. The comparative analysis revealed efficiency advantage of 1.966%, 1.41% and 1.17% in favor of DC as compared to AC for the scenarios considered. In the end future recommendations are presented to further enhance the efficiency of DC, thereby providing a concrete standing for power industry decision of adopting DC at distribution scale.