This table shows regional figures on the average consumption of energy (natural gas and electricity) of private dwellings broken down by type of dwelling and ownership for Nederland, group of provinces, provinces and municipalities. Besides, for total dwellings only, the share of heat distribution (district heating) has been added, because this is relevant for the interpretation of the height of the average consumption of natural gas.

Data available from: 2010

Status of the figures: All figures from 2010 - 2021 are definite. Figures of 2022 are provisional.

Changes as of October 2023: Provisional figures of 2022 have been added. Figures of 2021 have been updated. The category “Average consumption of electricity” is replaced by “Average supply of electricity” and a category “Average net supply of electricity” has been added.

When will new figures be published? A revision to the method of this statistic is currently underway, causing the table to be delayed. New figures will come in the 3rd quarter of the folowing year.

Residential customers use an average of about 1,000 kWh of electricity per month, with usage higher during hot summer months and lower in the winter. View tables show monthly average usage in kWh by month for residential customers starting in 2000. Tables include monthly fuel charges and electric bill amounts.

In 2023, the value of energy consumption per household in Bangkok and its vicinity amounted to ***** Thai baht. In that year, the average monthly energy consumption value per household in Thailand amounted to almost ***** Thai baht.

CN: Electricity Consumption: per Capita: Residential: Average data was reported at 987.000 kWh in 2022. This records an increase from the previous number of 869.000 kWh for 2021. CN: Electricity Consumption: per Capita: Residential: Average data is updated yearly, averaging 126.527 kWh from Dec 1980 (Median) to 2022, with 43 observations. The data reached an all-time high of 987.000 kWh in 2022 and a record low of 10.721 kWh in 1980. CN: Electricity Consumption: per Capita: Residential: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Energy Sector – Table CN.RBB: Electricity Consumption per Capita.

Dataset from Energy Market Authority. For more information, visit https://data.gov.sg/datasets/d_90009364978a4acd3132be18b4d23be2/view

This table contains 1155 series, with data for years 2011-2019 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...); Type of dwelling (7 items: Single-detached; Double; Row or terrace; Duplex; ...); Energy type (4 items: Total, all energy types; Electricity; Natural gas; Heating oil); Energy consumption (4 items: Gigajoules; Gigajoules per household; Proportion of total energy; Number of households).

Electricity consumption in the United States totaled ***** terawatt-hours in 2023, one of the highest values in the period under consideration. Figures represent energy end use, which is the sum of retail sales and direct use of electricity by the producing entity. Electricity consumption in the U.S. is expected to continue increasing in the next decades. Which sectors consume the most electricity in the U.S.? Consumption has often been associated with economic growth. Nevertheless, technological improvements in efficiency and new appliance standards have led to a stabilizing of electricity consumption, despite the increased ubiquity of chargeable consumer electronics. Electricity consumption is highest in the residential sector, followed by the commercial sector. Equipment used for space heating and cooling account for some of the largest shares of residential electricity end use. Leading states in electricity use Industrial hub Texas is the leading electricity-consuming U.S. state. In 2022, the Southwestern state, which houses major refinery complexes and is also home to nearly ** million people, consumed over *** terawatt-hours. California and Florida trailed in second and third, each with an annual consumption of approximately *** terawatt-hours.

Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

In 2023, the value of electricity consumption per household in Thailand amounted to *** Thai baht. In that year, the total energy consumption per household in the country reached almost ***** Thai baht.

The statistic represents the average annual electricity consumption of non-commercial customers in the United States between 1990 and 2017. In 2017, the average annual energy consumption per residential customer was ****** kilowatt hours.

Understanding the residential energy consumption patterns across multiple income groups under decarbonization scenarios is crucial for designing equitable and effective energy policies that address climate change while minimizing disparities. This dataset is developed using an integrated human-Earth system model, supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment at Pacific Northwest National Laboratory (PNNL).

GCAM-USA operates within the Global Change Analysis Model, which represents the behavior of, and interactions between, different sectors or systems, including the energy system, the economy, agriculture and land use, water, and the climate. GCAM is one of only a few integrated global human-Earth system models, also known as Integrated Assessment Models (IAMs), which address key processes in inter-linked human and earth systems and provide insights into future global environmental change under alternative scenarios (IAMC, 2022).

GCAM has global coverage with varying spatial disaggregation depending on the type of system being modeled. For energy and economy systems, 32 regions across the globe, including the USA as its own region, are modeled in GCAM. GCAM-USA advances with greater spatial detail in the USA region, which includes 50 States plus the District of Columbia (hereinafter “state”). The core operating principle for GCAM and GCAM-USA is market equilibrium. The model solves every market simultaneously at each time step where supply equals demand and prices are endogenous in the model. The official documentation of GCAM and GCAM-USA can be found at: https://jgcri.github.io/gcam-doc/toc.html

The dataset included in this repository is based on an improved version of GCAM-USA v6, where multiple consumer groups, differentiated by the average income level for 10 population deciles, are represented in the residential building energy sector. As of May 15, 2023, the latest officially released version of GCAM-USA has a single consumer (represented by average GDP per capita) in the residential sector and thus does not include this feature. This multiple-consumer feature is important because (1) demand for residential floorspace and energy are non-linear in income, so modeling more income groups improves the representation of total demand and (2) this feature allows us to explore the distributional effects of policies on these different income groups and the resulting disparity across the groups in terms of residential energy security. If you need more information, please contact the corresponding author.

Here, we ran GCAM-USA with the multiple-consumer feature described above under four scenarios over 2015-2045 (Table 1), including two business-as-usual scenarios and two decarbonization scenarios (with and without the impacts of climate change on heating and cooling demand). This repository contains the key output variables related to the residential building energy sector under the four scenarios, including:

• income shares by consumer groups at each state over 2015-2045 (Casper et al. 2022)
• residential energy consumption per capita by service and fuel, by state and income group, 2015-2045
• residential energy service output (energy consumption * technology efficiency) per capita by service, fuel, and technology, by state and income group, 2015-2045
• estimated energy burden (Eq.1), by state and income group, 2015-2045
• residential heating service inequality (Eq.2), by state, 2015-2045

Table 1

Eq. 1

\(Energy\ burden_i = \dfrac{\sum_j (service\ output_{i,j} * service\ cost_j)}{GDP_i}\)

for income group i and service j

Eq. 2

\(Residential\ heating\ service\ inequality = \dfrac{S_{d10}}{(S_{d1} +S_{d2} + S_{d3} + S_{d4})}\)

where S is the residential heating service output per capita of the highest income group (d10) divided by the sum of that of the lowest four income groups (d1, d2, d3, and d4), similar to the Palma ratio often used for measuring income inequality. A higher Palma ratio indicates a greater degree of inequality.

Reference

Casper, Kelly, Narayan, Kanishka B., O'Neill, Brian C., & Waldhoff, Stephanie. 2022. State level income distributions for net income deciles for the US for historical years (2011-2014) and projections for different SSP scenarios (2015-2100) (latest version obtained from the authors on April 6, 2023) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7227128

IAMC. 2022. The common Integrated Assessment Model (IAM) documentation [Online]. Integrated Assessment Consortium. Available: https://www.iamcdocumentation.eu/index.php/IAMC_wiki [Accessed May 2023].

This research was supported by the Grid Operations, Decarbonization, Environmental and Energy Equity Platform (GODEEEP) Investment, under the Laboratory Directed Research and Development (LDRD) Program at Pacific Northwest National Laboratory (PNNL).

PNNL is a multi-program national laboratory operated for the U.S. Department of Energy (DOE) by Battelle Memorial Institute under Contract No. DE-AC05-76RL01830.

This is grouped and aggregated electricity consumption data from the EtudELEC study conducted by the Observatoire du Transition Energétique Grenoble (OTE-UGA). If you find this dataset useful and like different groupings to be published or have any questions, please feel free to comment on the discussion dedicated to this dataset on the OTE forum . The EtudELEC study only studies electricity consumption from residential dwellings around France. The study involved over 400 homes (individual houses and apartments) and spanned ~11 months from 25th October 2022 to 1st October 2023. The data is collected from the smart meter data (Linky data) which is only available with a time-step of 30 minutes. The data is in average watts consumed in a half-hour period. For reasons for privacy in line with GDPR laws, personal data such as individual home consumption will be shared as aggregated datasets as opposed to individual data points. The data from the participants were aggregated based on the following groupings: - Type of heating used and type of residence (stand-alone house vs apartment) This dataset is best viewed in the "Tree" view below. A folder is created for each of the groupings and sub-folders exist for all the subsequent groups. Each group folder contains: - a table of the minimum, mean, and maximum of the average power consumed for each 30-minute period (W), and - a JSON file with aggregated demographics information (number of inhabitants in different age backets, socio-professional category, year of construction etc.) of the group The datasets will be updated on a yearly basis following the renewal of consent of the panel members. Il s'agit de données de consommation électriques groupées et agrégées issues de l'étude EtudELEC menée par l'Observatoire de la Transition Energétique (OTE-UGA). Si vous trouvez ce jeu de données utile et souhaitez que différents regroupements soient publiés, n'hésitez pas à écrire dans le topic sur le forum OTE. L'étude EtudELEC est une étude sur la consommation d'électricité des logements résidentiels en France. L'étude porte sur plus de 400 logements (maisons individuelles et appartements) et s'étend sur 11 mois du 25 octobre 2022 au 1 octobre 2023. Les données sont collectées à partir des données des compteurs intelligents (données Linky) qui ne sont disponibles qu'avec un pas de temps de 30 minutes. Les données sont exprimées en watts consommés en moyenne sur une période d'une demi-heure. Pour des raisons de confidentialité conformes aux lois RGPD, les données personnelles telles que la consommation individuelle des maisons seront partagées sous forme d'ensembles de données agrégées plutôt que de points de données individuels. Les données des participants ont été agrégées sur la base des regroupements suivants : - Type de chauffage utilisé et type de résidence (maison individuelle ou appartement). Cet ensemble de données est mieux visualisé dans l'arborescence ci-dessous. Un dossier est créé pour chaque groupe et des sous-dossiers existent pour tous les groupes suivants. Chaque dossier de groupe contient : - un tableau du minimum, de la moyenne et du maximum de la puissance moyenne consommée pour chaque période de 30 minutes (W), et - un fichier JSON avec des informations démographiques agrégées (nombre d'habitants dans différentes tranches d'âge, catégorie socioprofessionnelle, année de construction, etc. Les jeux de données seront mis à jour chaque année après le renouvellement du consentement des membres du panel.

In 2019, household consumption of electricity per capita in China stood at about 756 kilowatt hours. This was a more than six-fold increase in the per capita household electricity consumption compared to 2000.

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This statistic represents the average monthly residential consumption of electricity in East North Central in 2011. In Illinois, an average of 770 kilowatt hours of residential electricity were consumed per month.

Ghana: Energy consumption per capita, residential sector: The latest value from is Million Btu, unavailable from Million Btu in . In comparison, the world average is 0.00 Million Btu, based on data from countries. Historically, the average for Ghana from to is Million Btu. The minimum value, Million Btu, was reached in while the maximum of Million Btu was recorded in .

The data package provides average residential, commercial, and industrial electricity rates by zip code for both investor-owned utilities (IOU) and non-investor owned utilities. The datasets include information such as peak load, generation, electric purchases, sales, revenues, customer counts and demand-side management programs, green pricing and net metering programs, and distributed generation capacity.

Iran Electricity Consumption: Household data was reported at 18,339.000 kWh mn in Jun 2018. This records an increase from the previous number of 16,260.000 kWh mn for Mar 2018. Iran Electricity Consumption: Household data is updated quarterly, averaging 15,589.000 kWh mn from Jun 2008 (Median) to Jun 2018, with 41 observations. The data reached an all-time high of 28,016.100 kWh mn in Sep 2017 and a record low of 12,262.000 kWh mn in Mar 2012. Iran Electricity Consumption: Household data remains active status in CEIC and is reported by Ministry of Energy. The data is categorized under Global Database’s Iran – Table IR.RB002: Electricity Generation and Consumption.

Average Residential Consumption: by State: Northeast: Sergipe data was reported at 132.680 kWh in 2023. This records an increase from the previous number of 125.870 kWh for 2022. Average Residential Consumption: by State: Northeast: Sergipe data is updated yearly, averaging 116.340 kWh from Dec 2009 (Median) to 2023, with 15 observations. The data reached an all-time high of 132.680 kWh in 2023 and a record low of 102.000 kWh in 2009. Average Residential Consumption: by State: Northeast: Sergipe data remains active status in CEIC and is reported by Energy Research Company. The data is categorized under Brazil Premium Database’s Energy Sector – Table BR.RBC095: Electricity Consumption: Average Consumption.