This dataset contains detailed measurements of electric power consumption in a household over a span of nearly four years. Collected at a one-minute sampling rate, the data provides insights into various electrical quantities and sub-metering values for the household. The dataset includes 2,075,259 observations and covers a period from December 2006 to November 2010.

This dataset is ideal for time-series analysis, regression modeling, clustering, and other tasks related to energy consumption forecasting, anomaly detection, and pattern recognition. It provides a valuable resource for understanding household energy usage and behavior.

Column Descriptions Date

Type: Date Description: The date in dd/mm/yyyy format. Missing Values: No Time

Type: Categorical Description: The time in hh:mm:ss format. Missing Values: No Global_active_power

Type: Continuous Description: Household global minute-averaged active power (in kilowatts). Missing Values: No Global_reactive_power

Type: Continuous Description: Household global minute-averaged reactive power (in kilowatts). Missing Values: No Voltage

Type: Continuous Description: Minute-averaged voltage (in volts). Missing Values: No Global_intensity

Type: Continuous Description: Household global minute-averaged current intensity (in amperes). Missing Values: No Sub_metering_1

Type: Continuous Description: Energy sub-metering No. 1 (in watt-hours of active energy), related to the kitchen. Missing Values: No Sub_metering_2

Type: Continuous Description: Energy sub-metering No. 2 (in watt-hours of active energy), related to the laundry room. Missing Values: No Sub_metering_3

Type: Continuous Description: Energy sub-metering No. 3 (in watt-hours of active energy), related to an electric water heater and air-conditioner. Missing Values: No

Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2023. Between 1980 and 2023, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe have further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels determine the amount of electricity the average person requires in each country.

Abstract: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

Source: Georges Hebrail (georges.hebrail '@' edf.fr), Senior Researcher, EDF R&D, Clamart, France Alice Berard, TELECOM ParisTech Master of Engineering Internship at EDF R&D, Clamart, France

Data Set Information: This archive contains 2075259 measurements gathered in a house located in Sceaux (7km of Paris, France) between December 2006 and November 2010 (47 months). Notes:

(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.

Attribute Information:

date: Date in format dd/mm/yyyy time: time in format hh:mm:ss global_active_power: household global minute-averaged active power (in kilowatt) global_reactive_power: household global minute-averaged reactive power (in kilowatt) voltage: minute-averaged voltage (in volt) global_intensity: household global minute-averaged current intensity (in ampere) sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered). sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light. sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

Relevant Papers: N/A

Citation Request: This dataset is made available under the “Creative Commons Attribution 4.0 International (CC BY 4.0)” license

About Dataset I need help to analyze this data set with R code, if someone can help me I'd appreciate a lot and I'd send some money for his kindness. I really need how to do a regression and clustering manipulating this data. Sorry about the format, it's in text file. Thanks in advance :)

**Context: ** Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. Different electrical quantities and some sub-metering values are available.

Data Set Characteristics: Multivariate, Time-Series

Associated Tasks: Regression, Clustering

Data Set Information:

This archive contains 2075259 measurements gathered between December 2006 and November 2010 (47 months). Notes: 1.(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3.

2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows). All calendar timestamps are present in the dataset but for some timestamps, the measurement values are missing: a missing value is represented by the absence of value between two consecutive semi-colon attribute separators. For instance, the dataset shows missing values on April 28, 2007.

Attribute Information: 1.date: Date in format dd/mm/yyyy

2.time: time in format hh:mm:ss

3.global_active_power: household global minute-averaged active power (in kilowatt)

4.global_reactive_power: household global minute-averaged reactive power (in kilowatt)

5.voltage: minute-averaged voltage (in volt)

6.global_intensity: household global minute-averaged current intensity (in ampere)

7.sub_metering_1: energy sub-metering No. 1 (in watt-hour of active energy). It corresponds to the kitchen, containing mainly a dishwasher, an oven and a microwave (hot plates are not electric but gas powered).

8.sub_metering_2: energy sub-metering No. 2 (in watt-hour of active energy). It corresponds to the laundry room, containing a washing-machine, a tumble-drier, a refrigerator and a light.

9.sub_metering_3: energy sub-metering No. 3 (in watt-hour of active energy). It corresponds to an electric water-heater and an air-conditioner.

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.

Brazil Electricity Consumption: Household data was reported at 15,990.000 GWh in Feb 2025. This records an increase from the previous number of 15,637.000 GWh for Jan 2025. Brazil Electricity Consumption: Household data is updated monthly, averaging 6,642.500 GWh from Jan 1979 (Median) to Feb 2025, with 554 observations. The data reached an all-time high of 15,990.000 GWh in Feb 2025 and a record low of 1,647.000 GWh in Feb 1979. Brazil Electricity Consumption: Household data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Global Database’s Brazil – Table BR.RB001: Electricity Consumption. [COVID-19-IMPACT]

This data set is time series electricity use data from rural households using off-grid energy systems in Kenya. As well as indicating lighting electricity use for a real-world use case, it can give insight into active occupancy times in the mornings and evenings. This can support estimation of load profiles for higher tiers of the Multi-tier Framework for energy access by adding in load profiles for additional appliances.

Two solar nano-grids (SONGs) were built in two rural communities in Kenya, as part of the Solar Nano-grids project (EPSRC ref: EP/L002612/1). One aspect of the SONGs were battery-charging systems, in which batteries could be charged at a central solar hub, and used in households to power lighting and mobile phone charging. For each battery the electricity use was recorded in real-time between July 2016 and November 2016 inclusive.

The data consist of separate demand (use of battery in the home for lighting) and charging (charging at the central hub) profiles in csv files, individually for each household. The data are half-hourly measurements of average power used for the household lighting system (3 3W LED bulbs with wiring and switches). There is data for 51 households, ranging in length from 3 days to 5 months. Note that the data set is solely electricity use for the household lighting system, and does not include electricity use via the USB port that was present for charging mobile phones. The households are anonymised and are numbered in order of ascending number of days of data.

The household battery packs were Li-ion with capacity 62 Wh, and the data were recorded using a FRDM K-64F mbed embedded in each. 13 post-processing steps were required to process the data gathered in raw form from the batteries into energy profiles for individual households (see reference below). These included: correcting the timestamps caused by time drift or recalibration of the RTCs, attributing batteries to the correct household, addressing logging disruptions and inconsistent logging frequencies, imposing limits on power and duration of use to remove non-representative battery use, and testing loading conditions to remove abnormal energy use. The gaps in the data and varying lengths of the data are caused by: technical challenges with the batteries, meaning that they required frequent repairing; issues with the RTC on the microcontroller being reset; difficulty in attributing data to the correct household. Between 18th July - 1st August (approx.), the charging hub was shut down and so there is a gap in all energy profiles.

Graphical representations of the data for each household, and further information about the solar nano-grids project, the energy data, and the processing steps involved, can be found in Clements, A F. Data-driven approaches enabling the design of community energy systems in the Global South. DPhil Thesis. Department of Engineering Science, University of Oxford. 2019.

Note: This dataset has been superseded by the dataset found at "End-Use Load Profiles for the U.S. Building Stock" (submission 4520; linked in the submission resources), which is a comprehensive and validated representation of hourly load profiles in the U.S. commercial and residential building stock. The End-Use Load Profiles project website includes links to data viewers for this new dataset. For documentation of dataset validation, model calibration, and uncertainty quantification, see Wilson et al. (2022).

These data were first created around 2012 as a byproduct of various analyses of solar photovoltaics and solar water heating (see references below for are two examples). This dataset contains several errors and limitations. It is recommended that users of this dataset transition to the updated version of the dataset posted in the resources. This dataset contains weather data, commercial load profile data, and residential load profile data.

Weather The Typical Meteorological Year 3 (TMY3) provides one year of hourly data for around 1,000 locations. The TMY weather represents 30-year normals, which are typical weather conditions over a 30-year period.

Commercial The commercial load profiles included are the 16 ASHRAE 90.1-2004 DOE Commercial Prototype Models simulated in all TMY3 locations, with building insulation levels changing based on ASHRAE 90.1-2004 requirements in each climate zone. The folder names within each resource represent the weather station location of the profiles, whereas the file names represent the building type and the representative city for the ASHRAE climate zone that was used to determine code compliance insulation levels. As indicated by the file names, all building models represent construction that complied with the ASHRAE 90.1-2004 building energy code requirements. No older or newer vintages of buildings are represented.

Residential The BASE residential load profiles are five EnergyPlus models (one per climate region) representing 2009 IECC construction single-family detached homes simulated in all TMY3 locations. No older or newer vintages of buildings are represented. Each of the five climate regions include only one heating fuel type; electric heating is only found in the Hot-Humid climate. Air conditioning is not found in the Marine climate region.

One major issue with the residential profiles is that for each of the five climate zones, certain location-specific algorithms from one city were applied to entire climate zones. For example, in the Hot-Humid files, the heating season calculated for Tampa, FL (December 1 - March 31) was unknowingly applied to all other locations in the Hot-Humid zone, which restricts heating operation outside of those days (for example, heating is disabled in Dallas, TX during cold weather in November). This causes the heating energy to be artificially low in colder parts of that climate zone, and conversely the cooling season restriction leads to artificially low cooling energy use in hotter parts of each climate zone. Additionally, the ground temperatures for the representative city were used across the entire climate zone. This affects water heating energy use (because inlet cold water temperature depends on ground temperature) and heating/cooling energy use (because of ground heat transfer through foundation walls and floors). Representative cities were Tampa, FL (Hot-Humid), El Paso, TX (Mixed-Dry/Hot-Dry), Memphis, TN (Mixed-Humid), Arcata, CA (Marine), and Billings, MT (Cold/Very-Cold).

The residential dataset includes a HIGH building load profile that was intended to provide a rough approximation of older home vintages, but it combines poor thermal insulation with larger house size, tighter thermostat setpoints, and less efficient HVAC equipment. Conversely, the LOW building combines excellent thermal insulation with smaller house size, wider thermostat setpoints, and more efficient HVAC equipment. However, it is not known how well these HIGH and LOW permutations represent the range of energy use in the housing stock.

Note that on July 2nd, 2013, the Residential High and Low load files were updated from 366 days in a year for leap years to the more general 365 days in a normal year. The archived residential load data is included from prior to this date.

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.

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.

This table contains 165 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; ...) ; 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).

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.

This map shows electricity access in Asia and the Pacific. The data source is from the International Energy Agency’s World Energy Outlook. The International Energy Agency’s World Energy Outlook first constructed a database on electrification rates for WEO-2002. The database once again was updated for WEO-2015, showing detailed data on national, urban and rural electrification.

The general paucity of data on electricity access means that it must be gathered through a combination of sources, including: IEA energy statistics; a network of contacts spanning governments, multilateral development banks and country-level representatives of various international organisations; and, other publicly available statistics, such as US Agency for International Development (USAID) supported DHS survey data, the World Bank’s Living Standards Measurement Surveys (LSMS), the UN Economic Commission for Latin America and the Caribbean’s (ECLAC) statistical publications, and data from national statistics agencies. In the small number of cases where no data could be provided through these channels other sources were used. If electricity access data for 2013 was not available, data for the latest available year was used.

For many countries, data on the urban and rural breakdown was collected, but if not available an estimate was made on the basis of pre-existing data or a comparison to the average correlation between urban and national electrification rates. Often only the percentage of households with a connection is known and assumptions about an average household size are used to determine access rates as a percentage of the population. To estimate the number of people without access, population data comes from OECD statistics in conjunction with the United Nations Population Division reports World Urbanization Prospects: the 2014 Revision Population Database, and World Population Prospects: the 2012 Revision. Electricity access data is adjusted to be consistent with demographic patterns of urban and rural population. Due to differences in definitions and methodology from different sources, data quality may vary from country to country. Where country data appeared contradictory, outdated or unreliable, the IEA Secretariat made estimates based on cross-country comparisons and earlier surveys.

Reducing energy use is a key way in which we can help to reduce carbon emissions in the UK. Communal environments, such as shared offices, consume a large amount of energy. It is therefore important to examine people's perceptions and motivations to use and save energy. This study examines motivations to save energy at work and at home and the likely reactions to different cooperative scenarios around energy use. Data comprises: demographics, including whether participants have managerial responsibitilites, size and sector of organisation worked for; behavioural intentions for energy use at home and at work; motivations to save energy at work and at home; concern about climate change and energy security; experience of black outs, power cuts and air pollution.This project will investigate innovative ways of dividing up and representing energy use in shared buildings so as to motivate occupants to save energy. Smart meters (energy monitors that feed information back to suppliers) are currently being introduced in Britain and around the world; the government aims to have one in every home and business in Britain by 2019. One reason for this is to provide people with better information about their energy use to help them to save energy. Providing energy feedback can be problematic in shared buildings, and here we focus on workplaces, where many different people interact and share utilities and equipment within that building. It is often difficult to highlight who is responsible for energy used and difficult therefore to divide up related costs and motivate changes in energy usage. We propose to focus on these challenges and consider the opportunities that exist in engaging whole communities of people in reducing energy use. This project is multidisciplinary, drawing primarily on computer science skills of joining up data from different sources and in examining user interactions with technology, design skills of developing innovative and fun ways of representing data, and social science skills (sociology and psychology) in ensuring that displays are engaging, can motivate particular actions, and fit appropriately within the building environment and constraints. We will use a variety of methods making use of field deployments, user studies, ethnography, and small-scale surveys so as to evaluate ideas at every step. We have divided the project into three key work packages: 'Taking Ownership' which will focus on responsibility for energy usage, 'Putting it Together' where we will put energy usage in context, and 'People Power' where we will focus on creating collective behaviour change. In more detail, 'Taking Ownership' will explore how to identify who is using energy within a building, how best to assign responsibility and how to feed that back to the occupants. We know that simplicity of design is key here, as well as issues of fairness and ethics, and indeed privacy (might people be able to monitor your coffee drinking habits from this data?). 'Putting it Together' will consider different ways of combining energy data, e.g. joining this up across user groups or spaces, and combining energy data with other commonly available information, e.g. weather or diary data, so as to put it in context. We will also spend time considering the particular building context, the routines that currently exist for occupants, and the motivations that people have for using and saving energy within the building, in understanding how best to present energy information to the occupants. Our third theme, 'People Power' will focus on changing building user's behaviour collectively. We will examine how people interact around different energy goals, considering in particular cooperation and regulation, in finding out what works best in different contexts. The project then brings all aspects of research together in the use of themed challenge days where we promote specific energy actions for everyone in a building (e.g. switching off equipment after use) and demonstrate the impact that collective behaviour change can have. Beyond simply observing what works in this context through objective measures of energy usage, we will analyse when and where behaviour changes occurred and speak to the users themselves to find out what was engaging. These activities will combine to inform technical, design and policy recommendations for energy monitoring in workplaces as well as conclusions for other multi-occupancy buildings. Moreover, we will develop a tool kit to pass on to other companies and buildings so that others can use the findings and experience gained here. We will also explore theoretical implications of our results and communicate our academic findings to the range of disciplines involved

Energy Information Administration (2015). International Energy Statistics: Electricity Consumption | Country: Faroe Islands | Indicator: Total Electricity Net Consumption (Billion Kilowatthours), 1980-2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. [Data-file]. Dataset-ID: 004-014-026. Dataset: Provides statistics on electricity consumption by country, as available. For all countries except the United States, total electric power consumption = total net electricity generation + electricity imports - electricity exports – electricity transmission and distribution losses. For the United States, data are drawn from the Energy Information Administration Annual Energy Review, Table 1, which provides a total of electricity retail sales to ultimate customers by electric utilities and, beginning in 1996, other energy service providers; and direct use, ie, use of electricity that is self-generated, produced by either the same entity that consumes the power or an affiliate, and used in direct support of a service or industrial process located within the same facility or group of facilities that house the generating equipment. Data are reported as net consumption, which excludes the energy consumed by the generating units, as opposed to gross consumption. The dataset provides data for 220 countries, as available, on energy-related metrics, including total and crude oil production, oil consumption, natural gas production and consumption, coal production and consumption, electricity generation and consumption, primary energy, energy intensity, CO2 emissions and imports and exports for all fuels. Data are sourced from Energy Information Administration research, as well as from national and international agencies, listed at http://www.eia.gov/cfapps/ipdbproject/docs/sources.cfm. Category: Energy Resources and Industries, International Relations and Trade Source: Energy Information Administration The Energy Information Administration (EIA), created by Congress in 1977, is an independent statistical and analytical agency within the United States Department of Energy. Its mission is to provide policy-independent data, forecasts, and analyses to promote sound policy making, efficient markets, and public understanding regarding energy and its interaction with the economy and the environment. http://www.eia.doe.gov/ Subject: Energy Consumption, Electricity

This dataset includes synthetic household sizes and end-use energy intensities for countries around the world, spanning various Shared Socioeconomic Pathways (SSP) from 1960 to 2100. Historical data up to 2021 was utilized to calibrate an XGBoost algorithm, which allows for interpolation and extrapolation of data in one-year intervals up to 2021 and in five-year intervals between 2025 and 2100. Five SSPs were predicted: SSP1, SSP2, SSP3, SSP4, and SSP5.Household sizes were predicted for each country, covering the national average, urban areas, and rural regions. Harmonized household sizes from the CORESIDENCE project (https://www.nature.com/articles/s41597-024-02964-3) were used to train an XGBoost model with customized objective function and evaluation metrics to prevent rural and urban household sizes from being both greater or smaller than the national average.Each country's end-use energy intensity for cooking, lights, appliances, and water heating was predicted using an XGBoost model. The free version of the IEA's Energy End-Uses and Efficiency Indicators (https://www.iea.org/data-and-statistics/data-product/energy-efficiency-indicators-highlights) was used.The model's predictors were obtained from the World Bank Database (https://data.worldbank.org), and future projections for each SSP of the same predictors were obtained from the Wittgenstein Centre Human Capital Data Explorer (https://dataexplorer.wittgensteincentre.org/wcde-v3/).

This map shows electricity access in Africa. The data source is from the International Energy Agency’s World Energy Outlook. The International Energy Agency’s World Energy Outlook first constructed a database on electrification rates for WEO-2002. The database once again was updated for WEO-2015, showing detailed data on national, urban and rural electrification.

The general paucity of data on electricity access means that it must be gathered through a combination of sources, including: IEA energy statistics; a network of contacts spanning governments, multilateral development banks and country-level representatives of various international organisations; and, other publicly available statistics, such as US Agency for International Development (USAID) supported DHS survey data, the World Bank’s Living Standards Measurement Surveys (LSMS), the UN Economic Commission for Latin America and the Caribbean’s (ECLAC) statistical publications, and data from national statistics agencies. In the small number of cases where no data could be provided through these channels other sources were used. If electricity access data for 2013 was not available, data for the latest available year was used.

For many countries, data on the urban and rural breakdown was collected, but if not available an estimate was made on the basis of pre-existing data or a comparison to the average correlation between urban and national electrification rates. Often only the percentage of households with a connection is known and assumptions about an average household size are used to determine access rates as a percentage of the population. To estimate the number of people without access, population data comes from OECD statistics in conjunction with the United Nations Population Division reports World Urbanization Prospects: the 2014 Revision Population Database, and World Population Prospects: the 2012 Revision. Electricity access data is adjusted to be consistent with demographic patterns of urban and rural population. Due to differences in definitions and methodology from different sources, data quality may vary from country to country. Where country data appeared contradictory, outdated or unreliable, the IEA Secretariat made estimates based on cross-country comparisons and earlier surveys.

Egypt Electricity Consumption data was reported at 14,500.000 kWh mn in Dec 2024. This records a decrease from the previous number of 15,120.000 kWh mn for Nov 2024. Egypt Electricity Consumption data is updated monthly, averaging 10,574.000 kWh mn from Jan 1997 (Median) to Dec 2024, with 336 observations. The data reached an all-time high of 39,385.000 kWh mn in Sep 2012 and a record low of 3,737.230 kWh mn in Feb 1997. Egypt Electricity Consumption data remains active status in CEIC and is reported by Ministry of Electricity and Renewable Energy. The data is categorized under Global Database’s Egypt – Table EG.RB004: Electricity Consumption.

Italy Energy Consumption: Household data was reported at 48.400 TOE mn in 2017. This records an increase from the previous number of 46.900 TOE mn for 2016. Italy Energy Consumption: Household data is updated yearly, averaging 46.400 TOE mn from Dec 2001 (Median) to 2017, with 17 observations. The data reached an all-time high of 49.500 TOE mn in 2013 and a record low of 40.200 TOE mn in 2002. Italy Energy Consumption: Household data remains active status in CEIC and is reported by Unione Petrolifera. The data is categorized under Global Database’s Italy – Table IT.RB002: Energy Consumption: By Type.