This 2009 version represents the 13th 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 12,083 households selected at random using a complex multistage, area-probability sample design. The sample represents 113.6 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2009 derived from their American Community Survey (ACS).

This 2009 version represents the 13th 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 12,083 households selected at random using a complex multistage, area-probability sample design. The sample represents 113.6 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2009 derived from their American Community Survey (ACS).

Context

This 2009 version represents the 13th 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 12,083 households selected at random using a complex multistage, area-probability sample design. The sample represents 113.6 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2009 derived from their American Community Survey (ACS)

The csv data file is accompanied by a corresponding "Layout file", which contains descriptive labels and formats for each data variable. The "Variable and response codebook" file contains descriptive labels for variables, descriptions of the response codes, and indicators for the variables used in each end-use model.

This data set is provided in support of a forthcoming paper: "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," [1]. These files include electricity demand profiles for 200 households randomly selected among the ones available in the 2009 RECS data set for the Midwest region of the United States. The profiles have been generated using the modeling proposed by Muratori et al. [2], [3], that produces realistic patterns of residential power consumption, validated using metered data, with a resolution of 10 minutes. Households vary in size and number of occupants and the profiles represent total electricity use, in watts. The files also include in-home plug-in electric vehicle recharging profiles for 348 vehicles associated with the 200 households assuming both Level 1 (1920 W) and Level 2 (6600 W) residential charging infrastructure. The vehicle recharging profiles have been generated using the modeling proposed by Muratori et al. [4], that produces real-world recharging demand profiles, with a resolution of 10 minutes. [1] M. Muratori, "Impact of uncoordinated plug-in electric vehicle charging on residential power demand." Forthcoming. [2] M. Muratori, M. C. Roberts, R. Sioshansi, V. Marano, and G. Rizzoni, "A highly resolved modeling technique to simulate residential power demand," Applied Energy, vol. 107, no. 0, pp. 465 - 473, 2013. https://doi.org/10.1016/j.apenergy.2013.02.057 [3] M. Muratori, V. Marano, R. Sioshansi, and G. Rizzoni, "Energy consumption of residential HVAC systems: a simple physically-based model," in 2012 IEEE Power and Energy Society General Meeting. San Diego, CA, USA: IEEE, 22-26 July 2012. https//doi.org/10.1109/PESGM.2012.6344950 [4] M. Muratori, M. J. Moran, E. Serra, and G. Rizzoni, "Highly-resolved modeling of personal transportation energy consumption in the United States," Energy, vol. 58, no. 0, pp. 168-177, 2013. https://doi.org/10.1016/j.energy.2013.02.055

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.

The north shore of the estuary has a great wealth that is accessible in most municipalities: magnificent sandy beaches that extend for tens of kilometers. In order to minimize the deterioration of the most fragile sectors and to mitigate the negative impacts of the use of beaches by humans, the ZIP Committee and its partners decided that it was essential first and foremost to establish an action plan aimed at the protection and/or restoration of beaches experiencing the greatest sources of disturbance.

The objective of this report is to present the main issues specific to beaches on the North Shore. In order to meet this objective, in 2009, 26 beaches on the north shore of the estuary were visited and characterized. For each of them, the following information was noted: the nature of the soil, the relief, the vegetation present, the length and width of the accesses, the presence of reception and awareness-raising facilities and the morphology of each beach. Particular attention was paid to the observation of environmental issues. Subsequently, the measures and recommendations to be taken for the sustainable maintenance of these coastal habitats were identified.

The Canada Energy Regulator regulates the export of natural gas. Orders or licenses are required to export natural gas, including liquefied natural gas, from Canada. Holders of these authorizations report monthly on their activities to CER. LNG import and export activities are available by terminal from 2009 to August 2024. Data is delayed by approximately 2 months. Disclaimer: 1.) The Canada Energy Regulator (CER) stopped authorizing natural gas import activities in August 2022 as it is not a requirement under the Canadian Energy Regulator Act (see the CER’s 3 February 2023 letter - https://www.cer-rec.gc.ca/en/about/how-we-regulate/guidance/cera/gas-import-authorization-regulatory-change-no-new-import-authorizations-required.html). This impacted the natural gas (including liquefied natural gas) import data collected and published by the CER. Natural gas import data from 1985 to 2024 are published in the CER’s Open Government reports. Gas import data after 2022 did not reflect total import activities. Another set of natural gas import data is available through Statistics Canada’s Canadian International Merchandise Trade web application. (https://www150.statcan.gc.ca/n1/pub/71-607-x/71-607-x2021004-eng.html) 2.) The published reports do not include export data about value or price and purchaser of LNG submitted by LNG Canada Development Inc. as holder of Licence GL-330 in accordance with the Commission’s 31 July 2025 decision that this data is to receive confidential treatment for a period of five years (https://docs2.cer-rec.gc.ca/ll-eng/llisapi.dll/fetch/2000/90466/94153/552726/834773/4480637/4590556/C35803-1_LNG_Canada_Development_Inc._-_Application_for_Confidential_Treatment_of_Export_Licence_Reporting_Information_-_Decision_on_Confidentiality_-_A9K4W9.pdf?nodeid=4590439&vernum=-2).

The Canada Energy Regulator regulates the export of natural gas. Orders or licenses are required to export natural gas, including liquefied natural gas, from Canada. Holders of these authorizations report monthly on their activities to CER. LNG import and export activities are available by terminal from 2009 to August 2024. Data is delayed by approximately 2 months. Disclaimer: The Canada Energy Regulator (CER) stopped authorizing natural gas import activities in August 2022 as it is not a requirement under the Canadian Energy Regulator Act (see the CER’s 3 February 2023 letter - https://www.cer-rec.gc.ca/en/about/how-we-regulate/guidance/cera/gas-import-authorization-regulatory-change-no-new-import-authorizations-required.html). This impacted the natural gas (including liquefied natural gas) import data submitted to the CER. Since the CER stopped authorizing import activities, natural gas reports are based on incomplete data and do not reflect the total volumes imported. The CER’s natural gas import reports will be discontinued after October 2024. Historical data will remain on our website. Another set of natural gas import data is available through Statistics Canada’s Canadian International Merchandise Trade web application. (https://www150.statcan.gc.ca/n1/pub/71-607-x/71-607-x2021004-eng.html)

Abstract

Since the last half of the 1950s, the Household Budget Survey (HBS) has been released as a regular annual survey. Up to 1992, the Czechoslovak statistical office in Prague carried out implementation of the survey. From 1993 the Statistical Office of the Slovak Republic has been responsible for HBS in the independent Slovak Republic.

The Household Budget Survey provides information about living standards and social situation of private households, especially information on development and structure of their expenditures and incomes. Data is also used to obtain weights for Consumer Price Index and to estimate household expenditure for National Accounts. Following EUROSTAT recommendations for HBS, Classification of Individual Consumption According to Purpose (COICOP) is applied to code expenditure. The recommendations are published in "Household Budget Surveys in the EU: Methodology and recommendations for harmonisation, 2003." For income items, the survey follows Regulation (EC) No 1177/2003 of the European Parliament and the Council concerning community statistics on income and living conditions (EU SILC).

In 2004, the Statistical Office of the Slovak Republic introduced stratified random sampling, with monthly exchange of households.

Geographic coverage

National

Analysis unit

• Households,
• Individuals.

A household is defined as one or more persons fulfilling two conditions: - live together in the same dwelling, - participate together at expenditure, before all on housing and eating.

Universe

• Private households in the Slovak Republic.

Collective households, such as as monasteries, hospitals, collective homes, and prisons are not included in the survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Statistical Office of the Slovak Republic started applying stratified random sample for Household Budget Surveys in 2004.

Previously, the quota sampling was implemented. Such available information as planned wages and social incomes, planned distribution of consumption goods and services, and lower costs allowed using the quota sampling. But during the nineties the political, economic and social conditions in the Slovak Republic changed. Data, which were applied for the correct definition of sample quota of HBS, had to be estimated to a high degree. This was the reason the sampling technique was changed.

The new sample has the following characteristics: Sample size - approximately 4,700 households a year; Sample frame - household file produced from data of Population and Housing Census 2001; First stratum - administrative regions (in each region the same number of households was selected); Second stratum - size group of municipality (size group was defined by number of population; in each group households were proportionally selected in relation to proportional division of households in each administrative region); First stage - in each region municipalities were selected from each size group; Second stage - in each selected municipality or town, households were selected using systematic random sampling technique.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household diaries and personal interviews are used to collect data.

Household diary is filled in by a household during one month. The diary records current expenditure and income for the whole household.

Personal interviews gather information about household members, dwelling, household equipment, ownership of selected real estate, income, and expenditure on durable goods.