The U.S. Residential Energy Consumption Survey, administered by the U.S. Energy Information Administration (EIA), uses a nationally representative sample to collect information about home characteristics, household energy usage, and energy cost. The microdata at the household level from 2020, 2015, 2009, 2005, 2001, 1997, 1993,1990, and 1987, made available by the EIA for public use, were curated by Carnegie Mellon University Libraries to make it more accessible for data analysis.

Representative sample on the energy consumption of private households in Germany in cooperation with forsa. These households are comprehensively surveyed on their consumption of the energy sources used, their living conditions and the characteristics of the building they live in. Meter readings were also collected. Data is available for the years 2003, 2005 as a cross-section and for 2006-2008, 2008-2011 and 2010-2013 as a panel. This data can be matched using an ID.

Abstract

Because of the importance of the household sector and due to it's large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special household energy survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household sector and to provide data on energy consumption behavior in the society by type of energy.

This report presents data on various energy households indicators in the Palestinian Territory, and presents statistical data on electricity and other fuel consumption for the household sector, using type of fuel by different activities (cooking, heating, lighting, and water heating).

Geographic coverage

The Palestinian Territory.

Analysis unit

Households

Universe

The target population was all Palestinian households living in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame Sampling frame is a master sample from the Population, Housing and Establishment Census 1997. It consists of a list of enumeration areas, which were used as PSU's in the first stage of selection.

Sampling Design The sample of this survey is a sub-sample of Labour Force Survey (LFS) sample, that is conducted every 13 weeks. The total sample of LFS is about 7,559 households distributed over 13 weeks. The sample of the Household Energy Survey occupies six weeks of the first quarter 2005 of LFS.

Stratification: In designing the sample of LFS, four levels of stratification were made:

Stratification by governorate. Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee camps Stratification by locality size. Stratification by classifying localities, excluding governorate capitals, into three strata based on the ownership of households within these localities of durable goods.

Sample Unit: In the first stage, the sampling units are the enumerator areas (clusters) in the master sample. In the second stage, the sampling units are households.

Analysis Unit: Analysis units are composed of households.

Sample Size: The sample size is of (3,477) Palestinian households in West Bank and Gaza Strip, where this sample has been distributed according to the locality in urban areas, in rural areas and in refugee camps.

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

he data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: At this stage, data was entered into the computer using a data entry template developed in Access. The data entry program was prepared to satisfy a number of requirements such as: · To prevent the duplication of the questionnaires during data entry. · To apply integrity and consistency checks of entered data. · To handle errors in user friendly manner. · The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS.

Response rate

During fieldwork 3,477 families were visited in the Palestinian Territory, the end results for the interview become as following: (3022) complete questioner (34) traveling family (69) housing unit not existed (67) cases no body in the house (16) objection cases (185) housing unit abandoned (28) family can't give data (56) other cases During fieldwork 3,477 families were visited in the Palestinian Territory, There is 3,022 complete questioner, this percent was about 87%.

Sampling error estimates

It includes many aspects of the survey, mainly statistical errors due to the sample, and non statistical errors referring to the workers and tools of the survey. It includes also the response rates in the survey and their effect on the assumptions. This section includes:

Sampling Errors: These types of errors evolved as a result of studying a part of the society and not all of it. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

Non Sampling Errors: These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself which depends on type of estimation (b) diversity of sources (e.g. the interviewers, respondent, editors, coders, data entry operator …etc).

The sources of these errors can be summarized in: Some of the households were not in their houses and the interviewers couldn't meet them. Some of the households didn't show attention toward the questionnaire.
Some errors occurred due to the way the questions were asked by interviewers. Misunderstood of the questions by the respondents. Answering the questions related to consumption by making estimations.

Special Technical Notes: This part presents the important technical notes on the indicators presented in the main results of the survey:

In all calculations related to Gasoline, we dealt with the average of all available types of Gasoline.

Data appraisal

The data of the survey is comparable geographically and over time by comparing the data between different geographical areas to data of previous surveys.

Erhebung zum Energieverbrauch privater Haushalte in Deutschland in Zusammenarbeit mit forsa als repräsentative Stichprobe. Ziel dieser Studie ist die Gewinnung aussagekräftiger Informationen über den Energieverbrauch privater Haushalte in Deutschland. Diese Haushalte werden umfassend zu ihrem Verbrauch der jeweils benutzten Energieträger, den Wohnverhältnissen und den Charakteristika des bewohnten Gebäudes befragt. In der Befragung wurden zudem die Zählerstände erhoben. Diese zeigen den Energieverbrauch des Befragungsjahres. Es liegen Daten für die Jahre 2003, 2005 als Querschnitt und für 2006-2008 und 2008-2011 und 2011-2013 als Panel vor. Diese Daten können anhand einer ID aneinander gespielt werden.Dieser Datensatz bezieht sich auf die Daten des Jahres 2005. Randomly sampled survey on the energy consumption of private households in Germany. The data have been obtained in cooperation with forsa. The households were asked about the energy soures they rely on and their respective consumption, their living conditions, and characteristics of their dwelling. In addition, readings from the meters for asked. These show the energy consumption of the survey year. Data is available as cross-section data for the years 2003 and 2005, and as panels for the years 2006-2008, 2008-2011 and 2011-2013. Data can be merged using a household identifier. This dataset refers to the data for the year 2005. RWI-GRECS Selbst ausgefüllter Fragebogen: CASI Self-completed questionnaire:CASI Deutschsprachige Haushalte in Deutschland, die über ein Fernsehgerät und einen Telefonanschluss verfügen. German speaking households in Germany who have a television and a telephone connection. Mehrstufige, geschichtete und teilquotierte Zufallsstichprobe auf Basis des ADM-Mastersamples für Telefonstichproben für Haushalte, die 2005 nicht umgezogen sind.

Abstract

Energy has great importance due to its role in reflecting the country's economy, the people's welfare and their living standards. Also, energy data reflects the infrastructure situation.

In 1996, PCBS established an energy statistics program in order to develop a national plan for energy statistics and to provide data about energy in the Palestinian Territory. Taking into consideration the international recommendations of the United Nations in the field of energy and the special situation of the Palestinian Territory, energy indicators were formulated through a user-producer dialogue workshop held in March 1998. Energy statistics program implemented nine rounds of household energy survey during 1999-2005.

Because of the importance of the household sector and due to it's large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special household energy survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey. This survey aimed to provide data on energy consumption in the household sector and to provide data on energy consumption behavior in the society by type of energy.

Geographic coverage

West Bank and Gaza

Analysis unit

Households

Universe

The target population was all Palestinian households living in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a two-stage stratified cluster random sample.

Sampling Frame Sampling frame is a master sample from the Population, Housing and Establishment Census 1997. It consists of a list of enumeration areas, which were used as PSU's in the first stage of selection.

Sampling Design The sample of this survey is a sub-sample of Labour Force Survey (LFS) sample, that is conducted every 13 weeks. The total sample of LFS is about 7,559 households distributed over 13 weeks. The sample of the Household Energy Survey occupies six weeks of the second quarter 2006 of LFS.

Stratification In designing the sample of LFS, three levels of stratification were made: Stratification by governorate. Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee camps Stratification by locality size.

Sample Unit In the first stage, the sampling units are the enumerator areas (clusters) in the master sample. In the second stage, the sampling units are households.

Analysis Unit Analysis units are composed of households.

Sample Size The sample size is of (3,148) Palestinian households in West Bank and Gaza Strip, where this sample has been distributed according to the locality in urban areas, in rural areas and in refugee camps.

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: At this stage, data was entered into the computer using a data entered template written in CSpro. Data entry was done decentralized in Nablus, Hebron, Gaza Strip in addition to the central office in Al-Bireh. The data entry program was prepared to satisfy a number of requirements such as: · Duplication of the questionnaires on the computer screen. · Logical and consistency check of data entered. · Possibility for internal editing of question answers. · Maintaining a minimum of digital data entry and fieldwork errors. · User friendly handling. · Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

Response rate

During fieldwork 3,148 families were visited in the Palestinian Territory, There is 3,125 complete questioner. , this percent was about 86%.

Sampling error estimates

Sampling Errors These types of errors evolved as a result of studying a part of the society and not all of it. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

Non Sampling Errors These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself which depends on type of estimation (b) diversity of sources (e.g. the interviewers, respondent, editors, coders, data entry operator …etc).

Data appraisal

The data of the survey is comparable geographically and over time by comparing the data between different geographical areas to data of previous surveys.

Abstract

The overall objective of this survey was to obtain vital information on the performance of the main sectors of the Ugandan economy, their expenditure and use of electricity since 2005 - 2008 and use of other energy sources.

Specifically, the household survey was intended to reveal the impact of electricity shortages and electricity prices at the household level.

The survey findings will therefore be used as an input into two studies namely: “the Study to assess the Impact of the Electricity Supply Constraints on the Ugandan Economy” and “the Poverty and Social Impact Analysis (PSIA) of the Power Sector Reforms in Uganda”.

Geographic coverage

Since the study targeted households consuming grid electricity, households in Northern Uganda were not included due to the low coverage of grid electricity in the area.

Analysis unit

The Energy Use Survey had the following units of analysis: households and enterprises.

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey was implemented as a follow-up study on households that indicated that they consumed electricity during the 2005/06 Uganda National Household Survey (UNHS). Since the study targeted households consuming grid electricity, households in Northern Uganda were not included due to the low coverage of grid electricity in the area. Using this criterion a total of 470 households that reported consuming electricity in 2005/06 were randomly selected to form a panel sample. On average there were four(4) panel households per EA. Alongside these, an equivalent 470 non-consumers of electricity so as to constitute a paired sample. These additional households will later be used in the analysis as controls whose data will be used to eliminate the effect of pre-existing differences and to control sample bias. In total 840 households were targeted in 111 Enumeration Areas (EAs).

The Enumeration Areas were selected on basis of location of grid electricity consumers (panel households). The fact that most of the consumers are in urban areas explains why there were more urban EAs selected as compared to rural EAs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The Survey comprised two modules namely; (a) Household Questionnaire:- the purpose of this module is to collect relevant information on electricity access, consumption, payment and uses at household level (b) Enterprise Questionnaire:- to collect relevant information on electricity uses at household enterprise level

The household questionnaire covered: - Household Information on the Head and Spouse - Access to and utilization of Grid electricity - Alternatives to Grid electricity - Household Appliances - Household Consumption Expenditure

The enterprise questionnaire covered: - Household enterprises - Energy Use by household enterprises - Coping Mechanisms of Household Enterprises The content of the questionnaires was based on the recommendations of an M& E consultant who was engaged to guide the designing team on the appropriate questions for a survey intended to feed into a PSIA. A detailed report with M & E guidelines is attached in Annex II

Response rate

The overall response rate for the panel sample was 65 percent. However the control households had a response rate of about 83 percent. There were no significant differences in response rates between urban and rural areas.

Sampling error estimates

While the UNHS 2005/06 covered a large sample of about 7000 households. Only about 10% of these were reported to consume grid electricity. Further, some EAs had very few households that consume electricity making it un-economical to attempt to trace them. Indeed only EAs with atleast 3 consumers were selected for the Energy study. This greatly reduced the effective sample size of panel households to about 470.

The East of England accounted for the highest average household electricity consumption in Great Britain in 2022. That year, it added up to 3,720 kilowatt-hours per household. In comparison, households in the North East of England had the lowest average consumption, at 2,906 kilowatt-hours per household. End user consumption of electricity in the UK Despite continual annual declines in consumption since 2005, households have remained the largest electricity end-users in the UK since 2005. In 2020, amidst lockdown measures following the coronavirus outbreak, the gap between domestic and industrial consumption grew even wider. In 2022, domestic electricity consumption in the UK amounted to 96 terawatt-hours, while industrial consumption recorded the lowest figure of the century, at some 85 terawatt-hours. Declining electricity consumption in the UK Electricity consumption in the UK has seen a dramatic decrease in the past two decades, at least partially related to increased efficiency in equipment and distribution. In 2022, consumption from all electricity suppliers in the UK stood at 275 terawatt-hours. Projections indicate that the UK's electricity consumption will resume a growing trend after 2025, to surpass 32 million tons of oil equivalent in 2040.

Abstract

Energy has great importance due to its role in reflecting the country's economy, the people's welfare and their living standards. Also, energy data reflects the infrastructure situation.

In 1996, PCBS established an energy statistics program in order to develop a national plan for energy statistics and to provide data about energy in the Palestinian Territory. Taking into consideration the international recommendations of the United Nations in the field of energy and the special situation of the Palestinian Territory, energy indicators were formulated through a user-producer dialogue workshop held in March 1998. Energy statistics program implemented nine rounds of household energy survey during 1999-2005.

Because of the importance of the household sector and due to it's large contribution to energy consumption in the Palestinian Territory, PCBS decided to conduct a special household energy survey to cover energy indicators in the household sector. To achieve this, a questionnaire was attached to the Labor Force Survey.

This survey aimed to provide data on energy consumption in the household sector and to provide data on energy consumption behavior in the society by type of energy.

This report presents data on various energy households indicators in the Palestinian Territory, and presents statistical data on electricity and other fuel consumption for the household sector, using type of fuel by different activities (cooking, Baking, conditioning, lighting, and water Heating).

Geographic coverage

The Palestinian Territory.

Analysis unit

Households

Universe

The target population was all Palestinian households living in the Palestinian Territory.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is a two-stage stratified cluster random sample.

Sampling Frame Sampling frame is a master sample from the Population, Housing and Establishment Census 1997. It consists of a list of enumeration areas, which were used as PSU's in the first stage of selection.

Sampling Design The sample of this survey is a sub-sample of Labour Force Survey (LFS) sample, that is conducted every 13 weeks. The total sample of LFS is about 7,559 households distributed over 13 weeks. The sample of the Household Energy Survey occupies six weeks of the second quarter 2006 of LFS.

Stratification In designing the sample of LFS, three levels of stratification were made:

Stratification by governorate. Stratification by place of residence which comprises: (a) Urban (b) Rural (c) Refugee camps Stratification by locality size.

Sample Unit In the first stage, the sampling units are the enumerator areas (clusters) in the master sample. In the second stage, the sampling units are households.

Analysis Unit Analysis units are composed of households.

Sample Size The sample size is of (3,148) Palestinian households in West Bank and Gaza Strip, where this sample has been distributed according to the locality in urban areas, in rural areas and in refugee camps.

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire for the Household Energy Survey was based on the experiences of similar countries as well as on international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: At this stage, data was entered into the computer using a data entered template written in CSpro. Data entry was done decentralized in Nablus, Hebron, Gaza Strip in addition to the central office in Al-Bireh. The data entry program was prepared to satisfy a number of requirements such as: · Duplication of the questionnaires on the computer screen. · Logical and consistency check of data entered. · Possibility for internal editing of question answers. · Maintaining a minimum of digital data entry and fieldwork errors. · User friendly handling. · Possibility of transferring data into another format to be used and analyzed using other statistical analytic systems such as SPSS.

Response rate

During fieldwork 3,148 families were visited in the Palestinian Territory, There is 3,125 complete questioner. , this percent was about 86%.

Sampling error estimates

Sampling Errors These types of errors evolved as a result of studying a part of the society and not all of it. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

Non Sampling Errors These errors are due to non-response cases as well as the implementation of surveys. In this survey, these errors emerged because of (a) the special situation of the questionnaire itself which depends on type of estimation (b) diversity of sources (e.g. the interviewers, respondent, editors, coders, data entry operator …etc).

Data appraisal

The data of the survey is comparable geographically and over time by comparing the data between different geographical areas to data of previous surveys.

In the past decade, electricity consumption in the United Kingdom has been on the decline. Households have consistently ranked as the largest electricity final users in the country. In 2023, households in the UK consumed 93 terawatt-hours of electricity, or roughly 35 percent of the total consumption. Meanwhile, the industrial sector's consumption of electricity has fallen from 117 terawatt-hours in 2005 to 86 terawatt-hours in 2023.

The consumption of electricity in the United Kingdom has fluctuated on an annual and seasonal basis throughout the period depicted, and indicated a general downward trend. Demand was consistently higher in the winter months, peaking each year in January or December. In April 2024, electricity consumption in the UK amounted to **** terawatt-hours. Domestic users consume the most electricity Although the industrial sector's electricity demand was higher between 1990 and 2005, domestic consumers have since reclaimed their spot as the largest electricity end users in the UK. East and South East England were the regions with the highest domestic electricity consumption in Great Britain, with more than *** megawatt-hours consumed per household in 2022. Declining electricity demand in the UK Electricity consumption in the UK has seen a mostly continual decrease since the turn of the century. After peaking at ***** terawatt hours in 2005, consumption has fallen below *** terawatt hours in 2020. This is despite the UK's population growing by approximately seven million during this period. Projections on electricity consumption in the UK show that the decreasing trend will last until at least 2025.

Abstract The study estimates the structure of end-use electricity consumption by residential sector in the years 2005 and 2019, segregated by eachBrazilian geographic region. The method used data from the survey of Household Appliances and Consumption Habits from the years2004-2006 and 2018-2019 and applied statistical techniques. The results showedregional singularities in thedevelopment of the consumption structure. The maindevelopments in the estimated structure between the years 2005 and 2019 wereanincrease in the end-use consumption in the Environmental Comfort Appliances category, and the continued high participation of the consumption of theFood Conservation category. There was a decrease in the share of WaterHeating category end-use consumption in all regions, except in the South. The studyalsoshowed a predominance of end-use consumption ofappliances in the Environmental Comfort, Heating Water and Food Conservation categories, ranging from 82% to 91% according to the different regions. The findings indicate high potential for implementing energy efficiency measures, however, it must be stressed that regional consumption patterns must be considered in order to achieve effective results.

Retail residential electricity prices in the United States have mostly risen over the last decades. In 2023, prices registered a year-over-year growth of 6.3 percent, the highest growth registered since the beginning of the century. Residential prices are projected to continue to grow by two percent in 2024. Drivers of electricity price growth The price of electricity is partially dependent on the various energy sources used for generation, such as coal, gas, oil, renewable energy, or nuclear. In the U.S., electricity prices are highly connected to natural gas prices. As the commodity is exposed to international markets that pay a higher rate, U.S. prices are also expected to rise, as it has been witnessed during the energy crisis in 2022. Electricity demand is also expected to increase, especially in regions that will likely require more heating or cooling as climate change impacts progress, driving up electricity prices. Which states pay the most for electricity? Electricity prices can vary greatly depending on both state and region. Hawaii has the highest electricity prices in the U.S., at roughly 43 U.S. cents per kilowatt-hour as of May 2023, due to the high costs of crude oil used to fuel the state’s electricity. In comparison, Idaho has one of the lowest retail rates. Much of the state’s energy is generated from hydroelectricity, which requires virtually no fuel. In addition, construction costs can be spread out over decades.