Responding to a 2024 survey, data center owners and operators reported an average annual power usage effectiveness (PUE) ratio of 1.56 at their largest data center. PUE is calculated by dividing the total power supplied to a facility by the power used to run IT equipment within the facility. A lower figure therefore indicates greater efficiency, as a smaller share of total power is being used to run secondary functions such as cooling.

The average for 2022 based on 190 countries was 139.5 billion kilowatthours. The highest value was in China: 8349.31 billion kilowatthours and the lowest value was in Montserrat: 0.02 billion kilowatthours. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

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.

The combined electricity consumption capacity of data centers in Northern Virginia, United States, amounted to *** gigawatts in 2023. The second-largest concentration of data centers worldwide was in the Chinese capital city, Beijing, with a power capacity of some *** gigawatts.

In 2022, data centers in China, the United States, and the European Union consumed approximately *** terawatt-hours of electricity. By 2026, data centers in China will account for the largest electricity consumption, with an estimate of *** terawatt-hours.

Electricity use in data centers run by Google and Microsoft accounted for ** terawatt hours in 2023, greater than that of the country of Jordan. The training of AI models has heavily contributed to an increase in energy requirements, leading a number of big tech companies to consume more energy than countries.

Interannual Electricity Demand Calculator

Large parts of this code were originally developed by Lieke van der Most (University of Groningen) in the EU renewable energy modelling framework and release under MIT license. The original version of the code can be found here and is referenced below as [1]. This model has been validated against historical electricity demand data reported on the ENTSO-E transparancy platform.

We have made the following adjustments to the original version:

generate hourly instead of daily electricity consumption profiles

use snakemake for workflow management

trim repository to demand-related code and data

adjust code to accept cutouts from atlite for weather data

Purpose

Variations in weather conditions affect electricity demand patterns. This workflow generates country-level electricity consumption time series based on weather data using analysis by Lieke van der Most correlating historical electricity demand to temperature. This workflow first calculates a daily electricity demand based on the regression model developed in [1]. Subsequently, cumulative daily electricity demands are disaggregated using a hourly profile sampled from a random historical day (that is the same weekday) from the Open Power System Database. The resulting output/demand_hourly.csv file is compatible with the open-source electricity system model PyPSA-Eur.

Holidays are treated like weekend days. Data on national holidays across Europe are obtained using another repository by Aleksander Grochowicz and others that similarly computes artificial electricity demand time series: github.com/aleks-g/multidecade-data. The holidays are stored at input_files/noworkday.csv.

Installation and Usage

Clone the Repository

Download the demand_calculator repository using git.

/some/other/path % cd /some/path/without/spaces /some/path/without/spaces % git clone https://github.com/martacki/demand_calculator.git

Install Dependencies with conda/mamba

Use conda or mamba to install the required packages listed in environment.yaml.

The environment can be installed and activated using

.../demand_calculator % conda env create -f environment.yaml .../demand_calculator % conda activate demand

Retrieve Input Data

The only required additional input files are ERA5 cutouts which can be recycled from the PyPSA-Eur weather data deposit on Zenodo. Place the file europe-2013-era5.nc in the following location (and rename!):

./input_files/cutouts/europe-era5-2013.nc

Cutouts for other weather years than 2013 can be built using the build_cutout rule from the PyPSA-Eur repository.

Run the Workflow

This repository uses snakemake for workflow management. To run the complete workflow, execute:

.../demand_calculator % snakemake -jall all

After successfully running the workflow, the output files will be located in output/energy_demand named demand_hourly_{yr}.csv.

The years to compute can be modified directly in the Snakefile.

License

The file demand_hourly.csv is released under CC-BY-4.0 license.

The file src.zip is released under MIT license.

Changelog

2024-03-15: Extended date range from 1941 to 2023.

"Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world setting, is necessary. The REFIT electrical load measurements dataset includes whole house aggregate loads and nine individual appliance measurements at 8-second intervals per house, collected continuously over a period of two years from 20 houses. During monitoring, the occupants were conducting their usual routines. At the time of publishing, the dataset has the largest number of houses monitored in the United Kingdom at less than 1-minute intervals over a period greater than one year. The dataset comprises 1,194,958,790 readings, that represent over 250,000 monitored appliance uses. The data is accessible in an easy-to-use comma-separated format, is time-stamped and cleaned to remove invalid measurements, correctly label appliance data and fill in small gaps of missing data."

Data set: https://pureportal.strath.ac.uk/en/publications/an-electrical-load-measurements-dataset-of-united-kingdom-househo

Banner photo by @thejmoore on unsplash.com

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.

Data Center Power Market Outlook

The global data center power market size was valued at approximately USD 20 billion in 2023 and is expected to reach around USD 40 billion by 2032, growing at a compound annual growth rate (CAGR) of about 7.5% from 2024 to 2032. This growth can be attributed to the increasing demand for energy-efficient power solutions in data centers, which have become essential for the continuous and reliable operation of IT infrastructure. The rising adoption of cloud computing, the proliferation of big data, and the expansion of edge computing are key factors driving the market's expansion during the forecast period.

One of the primary growth factors in the data center power market is the exponential increase in data generation and storage needs. With the advent of emerging technologies like the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML), the volume of data generated has skyrocketed, necessitating the development of robust and efficient data center infrastructures. This surge in data has led to a heightened demand for data centers that can handle large-scale processing and storage requirements, subsequently driving the need for advanced power solutions to ensure seamless operations and minimize downtime.

Another significant driver of market growth is the shift towards sustainable and energy-efficient solutions. Governments and regulatory bodies worldwide are imposing stringent energy consumption and carbon emissions standards on data centers. This has compelled data center operators to adopt green energy solutions, such as advanced power distribution units (PDUs) and uninterruptible power supply (UPS) systems, to enhance energy efficiency. Moreover, the integration of renewable energy sources, like solar and wind power, into data center operations is gaining traction, further propelling the growth of the data center power market.

The increased focus on edge computing is also playing a crucial role in the market's expansion. As businesses seek to deliver faster and more efficient services to end-users, the deployment of edge data centers closer to the data source has become imperative. These edge data centers necessitate sophisticated power systems that can provide reliable and uninterrupted power supply in remote and often challenging environments. Consequently, the demand for innovative power solutions tailored to the requirements of edge computing is expected to witness significant growth in the coming years.

From a regional perspective, North America continues to dominate the data center power market, driven by the presence of major tech companies and a robust IT infrastructure. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, fueled by the rapid digital transformation initiatives, increasing internet penetration, and the expansion of cloud-based services in countries like China, India, and Japan. Europe, Latin America, and the Middle East & Africa are also expected to witness steady growth, supported by ongoing investments in data center infrastructure and the adoption of advanced power management solutions.

Component Analysis

The data center power market by component is segmented into solutions and services. The solutions segment encompasses products like uninterruptible power supply (UPS) systems, power distribution units (PDUs), generators, and transfer switches and switchgears. These solutions are critical for ensuring the uninterrupted operation of data centers, protecting against power outages, and optimizing energy consumption. The increasing deployment of hyperscale data centers and the rising demand for energy-efficient power solutions are driving the growth of the solutions segment.

UPS systems, in particular, are witnessing substantial demand due to their ability to provide emergency power to data centers during outages and stabilize power fluctuations. Innovations in UPS technology, such as the integration of lithium-ion batteries and modular designs, are further enhancing their efficiency and reliability. Additionally, PDUs are gaining traction for their role in distributing electrical power to various data center components while ensuring optimal load balancing and energy management.

The services segment includes installation, maintenance, and consulting services that ensure the smooth operation

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.

Pakistan Electricity Consumption: Total data was reported at 110,764.000 GWh in 2024. This records a decrease from the previous number of 114,300.000 GWh for 2023. Pakistan Electricity Consumption: Total data is updated yearly, averaging 71,541.500 GWh from Jun 1991 (Median) to 2024, with 34 observations. The data reached an all-time high of 116,816.000 GWh in 2021 and a record low of 31,534.000 GWh in 1991. Pakistan Electricity Consumption: Total data remains active status in CEIC and is reported by Ministry of Finance. The data is categorized under Global Database’s Pakistan – Table PK.RB006: Electricity Generation and Consumption.

This dataset provides a set of 18 load profiles with an hourly temporal resolution that represent main industrial and tertiary sectors in France for the year 2018.The ELMAS dataset is derived from a total of 55,730 consumption time series initially split into 424 business sectors and three levels of subscribed capacity. The customer’s field of activity follows the Statistical Classification of Economic Activities in the European Community (NACE), which is a four-digit industry standard classification used in the European Union composed of 21 sections, 88 divisions, 272 groups, and 615 classes. For anonymity concerns, the initial times series are averaged according to their NACE coding and level of subscribed capacity.Discrepancies between the temporal patterns of customers that belong to the same NACE section highlight the need to resort to another clustering approach. Thus, a K-means algorithm is used to gather the business groups sharing similar temporal patterns into 18 clusters. The resulting clustering shows that numerous NACE sections are scattered over various clusters, which increases the global heterogeneity of the clustering while spoiling the interpretation. The proportion of these dispersed NACE classes in terms of annual energy consumption remains low, which suggests that a manual reorganisation has little impact on the global consistency of the clusters. This manual reclassification is conducted in such a way that scattered NACE classes are gathered in the cluster that possesses the highest share of the considered NACE section. The energy consumption time series dataset represents a limited panel composed of 55,730 customers, which may bias the output load profiles in comparison with the whole French panel of industrial and tertiary customers. To fill this gap, Enedis provides the annual energy consumption of a wider range of customers for the year 2019. This annual energy consumption dataset is used to generate weights implemented in the clustering approach and to derive weighted average time series for the clusters.

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

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.

Electricity Trading Platform Market Outlook

The global electricity trading platform market is poised for substantial growth, with a market size of approximately USD 6.5 billion in 2023, projected to reach around USD 14.2 billion by 2032, reflecting a robust CAGR of 8.9% during the forecast period. This growth is fueled by various factors including the increasing penetration of renewable energy sources, advancements in smart grid technologies, and the rising need for energy efficiency and optimization.

One of the primary growth drivers for the electricity trading platform market is the increasing integration of renewable energy sources into the power grid. As countries worldwide strive to meet their sustainability goals and reduce carbon emissions, the adoption of renewable energy such as wind, solar, and hydroelectric power is accelerating. This shift necessitates sophisticated trading platforms to manage the intermittent and decentralized nature of renewable energy production, ensuring a balanced and efficient energy market.

Additionally, the advancements in smart grid technologies are playing a crucial role in the expansion of the electricity trading platform market. Smart grids leverage digital communication technology to detect and react to local changes in electricity usage, enhancing the efficiency and reliability of the power grid. These technologies enable real-time data exchange, advanced analytics, and automated control, all of which are essential for the effective functioning of electricity trading platforms. The integration of Internet of Things (IoT) devices and artificial intelligence (AI) further augments the capabilities of these platforms, facilitating better demand-response mechanisms and predictive maintenance.

Moreover, the growing demand for energy efficiency and optimization is driving the need for electricity trading platforms. With increasing energy costs and heightened awareness of environmental impacts, both consumers and businesses are seeking ways to optimize energy usage. Electricity trading platforms provide the tools and data analytics necessary to achieve this, enabling participants to buy and sell electricity based on real-time market conditions, thus maximizing efficiency and cost savings. This trend is particularly prominent in the industrial and commercial sectors, where energy consumption is substantial and the potential for optimization is significant.

Regionally, North America and Europe are leading the market due to their early adoption of renewable energy technologies and advanced grid infrastructures. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period. This is attributed to rapid industrialization, urbanization, and significant investments in smart grid projects across countries like China, India, and Japan. The Middle East & Africa and Latin America are also emerging markets, with increasing focus on renewable energy and infrastructural developments.

Type Analysis

The electricity trading platform market by type encompasses Day-Ahead Trading, Intraday Trading, Balancing Market, and Others. Day-Ahead Trading is one of the most prevalent types, where market participants commit to buy or sell quantities of electricity for the next day. This type of trading allows for better planning and scheduling of power generation and consumption, thereby enhancing grid stability. The increasing complexity of balancing supply and demand due to the integration of renewable energy sources has bolstered the need for efficient day-ahead trading mechanisms.

Intraday Trading, on the other hand, deals with the trading of electricity within the same day. This type of trading is gaining traction due to its ability to provide more flexibility and responsiveness to sudden changes in electricity demand or supply. With the rising penetration of variable renewable energy sources like solar and wind, intraday trading is becoming crucial for maintaining grid reliability and avoiding imbalances. The ability to make quick adjustments in response to real-time market signals makes it an essential component of modern electricity markets.

The Balancing Market is designed to ensure that the supply and demand of electricity are balanced in real-time. It plays a critical role in maintaining the stability and reliability of the power grid. Participants in the balancing market provide ancillary services such as frequency regulation and reserve power to mitigate short-term discrepancies between supply and demand. With the increasing penetration of intermittent renewa

With the ongoing energy transition, power grids are evolving fast. They operate more and more often close to their technical limit, under more and more volatile conditions. Fast, essentially real-time computational approaches to evaluate their operational safety, stability and reliability are therefore highly desirable. Machine Learning methods have been advocated to solve this challenge, however they are heavy consumers of training and testing data, while historical operational data for real-world power grids are hard if not impossible to access.

This dataset contains long time series for production, consumption, and line flows, amounting to 20 years of data with a time resolution of one hour, for several thousands of loads and several hundreds of generators of various types representing the ultra-high-voltage transmission grid of continental Europe. The synthetic time series have been statistically validated agains real-world data.

Data generation algorithm

The algorithm is described in a Nature Scientific Data paper. It relies on the PanTaGruEl model of the European transmission network -- the admittance of its lines as well as the location, type and capacity of its power generators -- and aggregated data gathered from the ENTSO-E transparency platform, such as power consumption aggregated at the national level.

Network

The network information is encoded in the file europe_network.json. It is given in PowerModels format, which it itself derived from MatPower and compatible with PandaPower. The network features 7822 power lines and 553 transformers connecting 4097 buses, to which are attached 815 generators of various types.

Time series

The time series forming the core of this dataset are given in CSV format. Each CSV file is a table with 8736 rows, one for each hourly time step of a 364-day year. All years are truncated to exactly 52 weeks of 7 days, and start on a Monday (the load profiles are typically different during weekdays and weekends). The number of columns depends on the type of table: there are 4097 columns in load files, 815 for generators, and 8375 for lines (including transformers). Each column is described by a header corresponding to the element identifier in the network file. All values are given in per-unit, both in the model file and in the tables, i.e. they are multiples of a base unit taken to be 100 MW.

There are 20 tables of each type, labeled with a reference year (2016 to 2020) and an index (1 to 4), zipped into archive files arranged by year. This amount to a total of 20 years of synthetic data. When using loads, generators, and lines profiles together, it is important to use the same label: for instance, the files loads_2020_1.csv, gens_2020_1.csv, and lines_2020_1.csv represent a same year of the dataset, whereas gens_2020_2.csv is unrelated (it actually shares some features, such as nuclear profiles, but it is based on a dispatch with distinct loads).

Usage

The time series can be used without a reference to the network file, simply using all or a selection of columns of the CSV files, depending on the needs. We show below how to select series from a particular country, or how to aggregate hourly time steps into days or weeks. These examples use Python and the data analyis library pandas, but other frameworks can be used as well (Matlab, Julia). Since all the yearly time series are periodic, it is always possible to define a coherent time window modulo the length of the series.

Selecting a particular country

This example illustrates how to select generation data for Switzerland in Python. This can be done without parsing the network file, but using instead gens_by_country.csv, which contains a list of all generators for any country in the network. We start by importing the pandas library, and read the column of the file corresponding to Switzerland (country code CH):

import pandas as pd
CH_gens = pd.read_csv('gens_by_country.csv', usecols=['CH'], dtype=str)

The object created in this way is Dataframe with some null values (not all countries have the same number of generators). It can be turned into a list with:

CH_gens_list = CH_gens.dropna().squeeze().to_list()

Finally, we can import all the time series of Swiss generators from a given data table with

pd.read_csv('gens_2016_1.csv', usecols=CH_gens_list)

The same procedure can be applied to loads using the list contained in the file loads_by_country.csv.

Averaging over time

This second example shows how to change the time resolution of the series. Suppose that we are interested in all the loads from a given table, which are given by default with a one-hour resolution:

hourly_loads = pd.read_csv('loads_2018_3.csv')

To get a daily average of the loads, we can use:

daily_loads = hourly_loads.groupby([t // 24 for t in range(24 * 364)]).mean()

This results in series of length 364. To average further over entire weeks and get series of length 52, we use:

weekly_loads = hourly_loads.groupby([t // (24 * 7) for t in range(24 * 364)]).mean()

Source code

The code used to generate the dataset is freely available at https://github.com/GeeeHesso/PowerData. It consists in two packages and several documentation notebooks. The first package, written in Python, provides functions to handle the data and to generate synthetic series based on historical data. The second package, written in Julia, is used to perform the optimal power flow. The documentation in the form of Jupyter notebooks contains numerous examples on how to use both packages. The entire workflow used to create this dataset is also provided, starting from raw ENTSO-E data files and ending with the synthetic dataset given in the repository.

Funding

This work was supported by the Cyber-Defence Campus of armasuisse and by an internal research grant of the Engineering and Architecture domain of HES-SO.