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.

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2022, figures for 2023 are revised provisional figures and figures for 2024 are provisional.

Changes as of July 2025: Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the outdated data from June 2025. The most recent figures are 5 percent higher for 2024 solar electricity production. These figures are in these two tables (in Dutch): - StatLine - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - StatLine - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update is scheduled in November 2025. From that moment all figures will be fully consistent again. We apologize for the inconvenience.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

This dataset provides values for ELECTRICITY PRODUCTION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Those countries have used the highest electricity in 2019, on the basis of that pie chart has been created so that it can be easier to find out which country was at the highest rank in the case of consuming electricity.

Analysis of ‘Power consumption in India(2019-2020)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/twinkle0705/state-wise-power-consumption-in-india on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

India is the world's third-largest producer and third-largest consumer of electricity. The national electric grid in India has an installed capacity of 370.106 GW as of 31 March 2020. Renewable power plants, which also include large hydroelectric plants, constitute 35.86% of India's total installed capacity. During the 2018-19 fiscal year, the gross electricity generated by utilities in India was 1,372 TWh and the total electricity generation (utilities and non-utilities) in the country was 1,547 TWh. The gross electricity consumption in 2018-19 was 1,181 kWh per capita. In 2015-16, electric energy consumption in agriculture was recorded as being the highest (17.89%) worldwide. The per capita electricity consumption is low compared to most other countries despite India having a low electricity tariff.

In light of the recent COVID-19 situation, when everyone has been under lockdown for the months of April & May the impacts of the lockdown on economic activities have been faced by every sector in a positive or a negative way. With the electricity consumption being so crucial to the country, we came up with a plan to study the impact on energy consumption state and region wise.

The dataset is exhaustive in its demonstration of energy consumption state wise.

Content

Data is in the form of a time series for a period of 17 months beginning from 2nd Jan 2019 till 23rd May 2020. Rows are indexed with dates and columns represent states. Rows and columns put together, each datapoint reflects the power consumed in Mega Units (MU) by the given state (column) at the given date (row).

Acknowledgements

Power System Operation Corporation Limited (POSOCO) is a wholly-owned Government of India enterprise under the Ministry of Power. It was earlier a wholly-owned subsidiary of Power Grid Corporation of India Limited. It was formed in March 2009 to handle the power management functions of PGCIL.

The dataset has been scraped from the weekly energy reports of POSOCO.

Inspiration

Extensive research on power usage in the country is what inspired us to compile the dataset. We are making it public along with our research of the same. This is our first step towards independent data-based research. We are open to suggestions, compliments and criticism alike.

Do leave an upvote if you found the dataset helpful. It keeps me motivated to keep working hard :)

--- Original source retains full ownership of the source dataset ---

The data has been sourced from the International Renewable Energy Agency (https://pxweb.irena.org/pxweb/en/IRENASTAT). The indicators on energy transition have been formulated to help users understand the progress in the adoption of renewable energy sources vis-à-vis the increasing energy requirements.Sources: International Renewable Energy Agency (IRENA) (2022), Renewable Energy Statistics 2022, https://pxweb.irena.org/pxweb/en/IRENASTAT; IMF Staff Calculations.Category: Mitigation,Transition to a Low-Carbon Economy Data series: Electricity GenerationElectricity Installed Capacity Metadata:Electricity generation: The gross electricity produced by electricity plants, combined heat and power plants (CHP) and the distribution generators measured at the output terminals of generation. It includes on-grid and off-grid generation, and it also includes the electricity self-consumed in energy industries; not only the electricity fed into the grid (net electricity generation). The indicator is expressed in the Dashboard in Gigawatt hours (GWh).Electricity Installed Capacity: The maximum active power that can be supplied continuously (i.e., throughout a prolonged period in a day with the whole plant running) at the point of outlet (i.e. after taking the power supplies for the station auxiliaries and allowing for the losses in those transformers considered integral to the station). This assumes no restriction of interconnection to the network. It does not include overload capacity that can only be sustained for a short period of time (e.g., internal combustion engines momentarily running above their rated capacity). For most countries and technologies, the data on installed capacity on the Dashboard reflects the capacity installed and connected at the end of the calendar year and are expressed in Mega Watts (MW). The renewable power capacity data shown in these tables represents the maximum net generating capacity of power plants and other installations that use renewable energy sources to produce electricity. For most countries and technologies, the data reflects the capacity installed and connected at the end of the calendar year. Pumped storage is included in total capacity but excluded from total generation. The capacity data are presented in megawatts (MW) and the generation data are presented in gigawatt-hours (GWh). All the data are rounded to the nearest one MW/GWh, with figures between zero and 0.5 shown as a 0.

Detailed, accurate and timely data and statistics are essential for the monitoring and evaluation of renewable energy policies and deployment. IRENA helps analysts, policy makers and the public make informed decisions by providing access to comprehensive and up-to-date renewable energy data. IRENA publishes detailed statistics on renewable energy capacity, power generation and renewable energy balances. This data is collected directly from members using the IRENA Renewable Energy Statistics questionnaire and is also supplemented by desk research where official statistics are not available. Renewable power-generation capacity statistics are released annually in March. Additionally, renewable power generation and renewable energy balances data sets are released in July. IRENA’s statistics unit helps members to strengthen their data collection and reporting activities through training and methodological guidance. Member countries are encouraged to participate in this process. Explore IRENA data and statistics by browsing a wide range of topics such as Capacity and Generation, Costs, Finance and more on the menu.

China Electricity Consumption: per Capita: Average data was reported at 6,257.000 kWh in 2022. This records an increase from the previous number of 6,032.000 kWh for 2021. China Electricity Consumption: per Capita: Average data is updated yearly, averaging 1,066.997 kWh from Dec 1978 (Median) to 2022, with 45 observations. The data reached an all-time high of 6,257.000 kWh in 2022 and a record low of 261.265 kWh in 1978. China Electricity Consumption: per Capita: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCB: Electricity Summary.

This dataset provides values for ELECTRICITY PRICE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

For several years, domestic electricity consumption has been growing (the average annual growth rate of electricity consumption for 2005-2010 is 2.75%). The increase in demand for electricity is related to the continuous development of the Polish economy, the enrichment of society and the growing, year by year, industrial production. It should be noted that electricity consumption is related to the GDP per capita, the general economic situation and the modernization of the economy. The following dataset shows that there is a correlation between electricity consumption per capita and the GDP of a given country. Therefore, it should be assumed that the Polish economy, which is developing despite the global financial crisis and the European crisis of public debt and the financial sector, will become an increasingly larger energy consumer.

About the Project Increasing energy productivity holds some of the greatest possibilities for enhancing the welfare countries get out of their energy systems. It also recasts energy efficiency in terms of boosting competitiveness and wealth, more powerfully conveying its profound benefits to society. KAPSARC and UNESCWA have initiated this project to explore the energy productivity potential of the Arab region, starting with the six GCC countries and later extending to other countries. Aimed at policymakers, this project highlights the social gains from energy productivity investments, where countries are currently at, and pathways to achieving improved performance in this area. Key Points This paper describes our analysis of the cost-effectiveness of designing and retrofitting residential buildings in Bahrain and outlines our analytical approach. The study focuses on residential buildings since households consume more than 48 percent of electricity used in the country. As expected, residential buildings constitute the vast majority of Bahrain’s building stock, with about 76 percent of the total and projected annual growth in energy consumption of around 3 percent in the next few years. The optimization analysis outlined in this paper assesses the potential benefits from retrofitting both individual buildings and the entire national building stock, as well as the benefits of applying proven measures and technologies to improve the energy efficiency of the building sector. Our conclusions are: The development and enforcement of a more stringent energy efficiency code can potentially improve the energy efficiency of the new building stock with a reduction of more than 320 GWh in annual electricity consumption and 87 MW in peak demand. Retrofitting the existing building stock in Bahrain has the potential to cost-effectively reduce energy consumption in the building sector by 62 percent, with a 55 percent reduction in peak electricity demand compared with the business as usual scenario. The avoided costs of building new power plants would be sufficient to offset the implementation costs for a basic level of energy retrofitting of existing residential buildings. We estimate that as much as 31,700 job-years of employment can be created when retrofitting the existing building stock. More than 3,000 jobs would be needed annually in order to retrofit existing buildings over a 10-year period.

Abstract copyright UK Data Service and data collection copyright owner. The International Energy Agency (IEA) datasets published by the Energy Statistics Division (ESD) contain annual, and a small amount of quarterly and monthly time series data from 1960 onwards. The database covers energy production, trade, stocks, transformation, consumption, prices and taxes as well as on greenhouse gas emissions for over 30 OECD Member countries and over 100 non-OECD countries worldwide. In OECD Member countries the data are collected by official bodies (most often the national statistics office in each country) from firms, government agencies and industry organisations and are then reported to the IEA using questionnaires to ensure international comparability. In non-OECD countries the data are collected directly from government and industry contacts and from national publications. The International Energy Agency (IEA) Electricity Information database contains a time series of electricity statistics of OECD countries for supply, consumption, trade, capacity and price, and some projection data corresponding to the data shown in Part II of the annual IEA publication Electricity Information. Where available, all tables from Part I of the publication are also included. Data are based on submissions from national administrations to the Secretariat. The database contains a time series of annual electricity and heat data for Organisation for Economic Co-operation and Development (OECD) countries from 1960 onwards. The Electricity Information database is structured into eight tables. These data were first provided by the UK Data Service in June 2005 and are updated semi-annually. Main Topics: Topics covered include: electricity supplyelectricity consumptionelectricity tradeelectricity capacityelectricity priceselectricity forecasts

Global Total Support on End-Use Electricity for Consumers by Country, 2023 Discover more data with ReportLinker!

Content

4 of these datasets outline the quantity of terawatt hours (TWh) produced through various sources of energy, comparing both renewable and non-renewable sources, while highlighting the renewable use of the top 20 countries. The Renewables Power Generation dataset includes a 1997-2017 timeline that outlines the progress of the main renewable energy sectors : Hydro, Wind, Biofuel, Solar PV, and Geothermal. Additionally, the Top 20 Countries Power Generation dataset includes the national data for each of the renewable categories as outlined above. The last 2 datasets include the global TWh generated from renewable and non-renewable sources.

In the latest version, I added two datasets which contain the global consumption figures on national and continental/international group levels, which help provide context about the quantity of energy required, how that is changing over time, and how we are doing in terms of transitioning from non-renewable to renewable energy use.

Source

Renewable Energy: Reddy, Vamsi., Kalananda, Aala., Komanapalli, Narayana. "Nature Inspired Optimization Algorithms for Renewable Energy Generation, Distribution and Management - A Comprehensive Review. 2021.

Consumption: https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html (data converted from mTOE to TWh)

Inspiration

As temperatures rise and storms grow more fierce, improving the efficiency and increasing the use of renewable energy sources is critical. In turn, understanding which nations are leading the way and which require more immediate transformations will help target efforts and hopefully, reach global goals.

Which types of renewables are improving the fastest? Which countries using which types of renewables? At the increasing rate of returns on renewables, how long will it take to meet global demands and eliminate non-renewables, or atleast, break 50%?

The SDG Indicator 7.1.1: Access to Electricity, 2023 Release data set, part of the Sustainable Development Goal Indicators (SDGI) collection, measures the proportion of the population with access to electricity for a given statistical area. UN SDG 7 is "ensure access to affordable, reliable, sustainable and modern energy for all". Tracking SDG 7: The Energy Progress Report estimated that in 2019, 759 million people around the world lacked access to electricity. Moreover, due to current policies and the detrimental effects of the COVID-19 crisis, it is predicted that by 2030, 660 million people will still not have access to electricity, with a majority of these people residing in Sub-Saharan Africa. As one measure of progress towards SDG 7, the UN agreed upon SDG indicator 7.1.1. The indicator was computed as the proportion of WorldPop gridded population located within illuminated areas defined by annual VIIRS Nighttime Lights Version 2 (VNL V2) data. The SDG indicator 7.1.1 data set provides estimates for the proportion of population with access to electricity for 206 countries and 45,979 level 2 subnational Units. The data set is available at both national and level 2 subnational resolutions.

Analysis of ‘Global Energy Consumption & Renewable Generation’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/jamesvandenberg/renewable-power-generation on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Content

4 of these datasets outline the quantity of terawatt hours (TWh) produced through various sources of energy, comparing both renewable and non-renewable sources, while highlighting the renewable use of the top 20 countries. The Renewables Power Generation dataset includes a 1997-2017 timeline that outlines the progress of the main renewable energy sectors : Hydro, Wind, Biofuel, Solar PV, and Geothermal. Additionally, the Top 20 Countries Power Generation dataset includes the national data for each of the renewable categories as outlined above. The last 2 datasets include the global TWh generated from renewable and non-renewable sources.

In the latest version, I added two datasets which contain the global consumption figures on national and continental/international group levels, which help provide context about the quantity of energy required, how that is changing over time, and how we are doing in terms of transitioning from non-renewable to renewable energy use.

Source

Renewable Energy: Reddy, Vamsi., Kalananda, Aala., Komanapalli, Narayana. "Nature Inspired Optimization Algorithms for Renewable Energy Generation, Distribution and Management - A Comprehensive Review. 2021.

Consumption: https://yearbook.enerdata.net/total-energy/world-consumption-statistics.html (data converted from mTOE to TWh)

Inspiration

As temperatures rise and storms grow more fierce, improving the efficiency and increasing the use of renewable energy sources is critical. In turn, understanding which nations are leading the way and which require more immediate transformations will help target efforts and hopefully, reach global goals.

Which types of renewables are improving the fastest? Which countries using which types of renewables? At the increasing rate of returns on renewables, how long will it take to meet global demands and eliminate non-renewables, or atleast, break 50%?

--- Original source retains full ownership of the source dataset ---

The domain of interest is Energy; however, the focus is to observe the trends between the different sources used for electricity generation among Canada and its provinces from 2005 to 2016, and to compare the trends for electricity generation to electricity consumption in Canada from 2005 to 2015. The main problem that will be investigated is how much of a particular source is used for electricity generation in Canada over these eleven years and what is the least and most used source of electricity generation over Canada. It will also be observed whether the proportion of electricity generated by each source in Canada during 2016, is consistent with the proportion of electricity generated by each source in every province. Additionally electricity consumption for the provinces will be studied to determine which province consumes the most and least amounts of electricity in Canada. The significance of this problem is to understand which sources are highly used to generate electric power in the provinces and in Canada. If a source is being used the most in Canada and in the provinces, it will lead us to find possible ways to generate electricity from the least used sources, so the country and its provinces do not depend on one source for electric power. It will also be observed if the electricity generation by each province has increased, decreased or remain constant from 2005-2016. From this data we can also infer which province generates the most and least amount of electric power and determine which abundant resources are available to each province for its electricity generation. Moreover, by comparing the trends for electricity consumption and electricity generation it will be observed if any province consumes more electricity than it generates. If so we can find ways to provide that province with more electrcity by importing it from other provinces.

Output generated by the RAMP engine for use in the Sector-Coupled Euro-Calliope model. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.

• Cooking energy demand profiles (ramp-cooking-profiles): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the RAMP model [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.
• Electric vehicle plug-in profiles (ramp-ev-plugin-profiles): Profiles of the percentage of parked electric vehicles, stochastically generated using the RAMP-Mobility model [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.
• Electric vehicle energy consumption profiles (ramp-ev-consumption-profiles): Profiles of the electricity consumption of electric vehicles, stochastically generated using the RAMP-Mobility model [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.

* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE

** (*) + BGR, SRB

*** ALB, MKD, GRC, CYP, BIH, MNE, ISL

[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. Energy 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.

[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. Applied Energy 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.

Global Total Support on End-Use Electricity for Residential Share by Country (Million US Dollars), 2023 Discover more data with ReportLinker!

The data contained in this file is derived from the study published in https://doi.org/10.3390/en13153939.

For each of the studied countries, hourly data of the selected heating season (2018-2019) is presented, which includes: Primary energy, Cost and GHG emissions of a Heat pump and the most common non-electric heating system of the country.

One file per country is provided as a xlsx file with the following hourly features:

• Date
• Hour
• Heat demand: Building heating demand in kWh
• COPR32: Instant COP of the HP with R32 working fluid
• COPR410a: Instant COP of the HP with R410a working fluid
• ConElec: Electric consumption of the heat pump in kWh
• ConNE: Non-electric systems consumption
• €.elec: Cost of the electric consumption in €
• €.NE: Cost of the Non-electric systems consumption in €
• Price difference: Price difference between both systems in €
• PE.elec: Primary energy of the electricity consumption in KWh
• PE.NE: Primary energy of the non-electric systems consumption in KWh
• GHG.elec: GHG emissions of the electricity consumption in kg
• GHG.NE: GHG emissions of the electricity consumption in kg