Germany is the largest electricity consumer in the European Union (EU), with demand exceeding *** terawatt-hours in 2024. France followed closely in second, with a demand of *** terawatt-hours. In contrast, the Mediterranean islands of Cyprus and Malta had the lowest power demands in the EU that year.

In 2023, Europe's electricity consumption per capita amounted to nearly six megawatt-hours, down from 6.1 megawatt-hours per person in the previous year. Across the continent, electricity use varies greatly. In 2023, Iceland had the largest electricity consumption per capita in Europe, at around 52 megawatt-hours per person.

To achieve the decarbonization goal, electricity consumption in the European Union is forecast to increase in the coming years. According to the Fit for 55 (FF55) scenario, the total electricity demand of the EU will reach almost 3.6 petawatt-hours by 2030 and 4.5 petawatt-hours by 2050. EU's estimated electricity demand inspired by the REPowerEU and Radical Action scenario is larger, surpassing five petawatt-hours by 2050 in the second case.

This statistic displays the demand for primary energy in the European Union (EU-28) in 2016 and 2040, by fuel type. By 2040, the demand for coal is predicted to fall from *** Mtoe to ** Mtoe, showing a clear move away from coal. In comparison, the demand for renewable energy is projected to increase from *** Mtoe to *** Mtoe.

The Nordic countries of Iceland, Norway, Finland, and Sweden recorded the largest electricity demand per capita in Europe over the last few years. In 2023, Iceland’s per capita electricity demand averaged at almost 52 megawatt-hours per person, more than twice the consumption of runner-up Norway. Meanwhile, France, the largest electricity producer among the European Union countries, had an average per capita consumption of seven megawatt-hours in 2024. Why is electricity consumption so high in Iceland? The intense electricity consumption in Iceland stems from a combination of factors. On the one hand, due to its abundant natural resources, hydro and geothermal are the main sources of electricity generation in Iceland, allowing the country to produce power at high reliability and very low costs. Meanwhile, on the demand side, Iceland is home to some particularly energy-intensive industries – in 2023, it ranked among the largest aluminum smelter producers worldwide. To top it off, electricity demand is also high on the household sector side, due to the Nordic country’s long, dark, and cold winters. A similar combination of factors can also be found in Norway. How cheap is electricity in Iceland? In 2024, household electricity prices in Iceland averaged between 18 and 22.2 euro cents per kilowatt-hour, depending on the level of demand. In comparison to the average household electricity price in the European Union in the same period, it was around 40 percent cheaper. Iceland had one of the lowest residential electricity prices in Europe. On the other side of the spectrum, Germany, Denmark, and Belgium reported the highest prices in the region.

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.

France and Germany dominated Europe's electricity production landscape in 2023, generating over ***** terawatt-hours combined. This substantial output underscores the critical role these two nations play in powering the continent. The European Union as a whole produced some ***** terawatt-hours of electricity, highlighting the region's significant energy capacity. Energy front-runners in Europe Germany's role in European electricity goes beyond production, as it is also the largest consumer in the EU, with demand exceeding *** terawatt-hours in 2024. France followed closely with a demand of *** terawatt-hours. While France and Germany led in overall production and consumption, Nordic countries were setting the pace for clean energy adoption. Sweden and Finland had the largest electricity production from clean sources in 2024, attributing their success to a mix of hydropower, nuclear, and wind energy. Energy storage in Europe Renewable and nuclear energy generated significantly more electricity than fossil fuels in Europe throughout 2024. To support the energy transition and meet the large power demand, European countries such as Germany and Italy have invested heavily in energy storage capacity, with both countries boasting approximately *** gigawatts of installed capacity in 2024.

Under the Energy Efficiency Directive and the Renewable Energy Directive, EU Member States were required to report national figures and plans on heat and cold by the end of June 2024. This is in line with the European Energy Union's strategy to achieve carbon neutrality by 2050. The main products are maps for the territory of Flanders with the heat demand at the level of the municipalities and the statistical sectors, maps of the existing and planned heat networks and finally also locations of potential heat supply points. The study was carried out by the Flemish Energy and Climate Agency. You can consult the accompanying report here: https://www.vlaanderen.be/building-living-and-energy/green-energy/heat map. For the update of the detailed heat demand within Flanders, the data of consumption year 2020 or 2021 are not representative, because of the corona crisis (lower mobility, lower industrial activity) and the energy crisis with a major impact on energy demand. Therefore, the global analysis was carried out again on the basis of the demand figures 2019 and the consumption data for 2022 were not retrieved from Fluvius.In order to give an indication of the evolution of the heat demand, it was chosen to calculate a ‘Flemish rescaling coefficient’. This coefficient compares the total useful heat in Flanders (which was previously described in Chapter 2) between the data years 2022 and 2019. The rescaling coefficient is 0.915. This data layer is the result of a rescaling of the heat demand density map at the level of the statistical sectors from 2019 with the said rescaling coefficient. The sectors where the geometry has undergone significant changes, which could have a strong impact on the result, have not been retained in the final data layer.

In 2023, Germany recorded the most considerable fall in electricity demand within the European Union, amounting to over ** terawatt-hours. Meanwhile, Ireland recorded the largest increase in demand of **** terawatt-hours. On average, demand for electricity across the EU fell between 2022 and 2023. Electricity demand in the European Union Despite recording the largest growth in the decade, Malta still exhibited the lowest electricity demand in the European Union in 2022. The Mediterranean archipelago’s electricity consumption stood at less than * terawatt-hours that year. In contrast, Germany – with a population estimated at more than ** million inhabitants – was the largest consumer in the region that year, with more than *** terawatt-hours, or roughly ** percent of the total EU’s electricity demand. Meeting the demand Overall, the biggest electricity consumers in the European Union are also the largest electricity producers in the region, with Germany and France taking the lead. Nevertheless, while Italy also ranked amongst the top 5 producers, the country's output falls short of meeting its demand. In recent years, Italy was the EU's largest electricity net importer, at more than ** terawatt-hours.

Power demand forecasting dataset

Monthly data from two sets of series across European economies: (i) Gross Inland Natural Gas Consumption (in terajoules, TJ); and (ii) Energy Supplied (in gigawatt-hour, GWh). Data for the former were collected from the Statistical Office of the European Union database (EUROSTAT, 2019). Data for the latter were compiled from the International Energy Agency (IEA) Monthly Electricity Statistics reports, which provide information on energy production and trade for all OECD Member Countries (IEA, 2019).

References: EUROSTAT (2019). European Statistics supply of gas – gross inland consumption – monthly data. https://ec.europa.eu/eurostat/web/energy/data/database. Accessed: 2019-10-02. IEA (2019). International Energy Agency monthly electricity statistics. http://www.iea.org/statistics/monthlystatistics/monthlyelectricitystatistics/. Accessed: 2019-10-02.

This dataset represents the future time series of electricity demand for European countries from 2023 to 2100, aligning with the findings presented in our paper 'Future Electricity Demand for Europe: Unraveling the Dynamics of the Temperature Response Function,' published in Applied Energy.

Comprising more than 200 .CSV files, the dataset includes electricity demand data for 36 European countries, with each year being presented as a distinct .CSV file. Data for all years in each country are then compressed in a single .ZIP file.

The column explanation is as below:

• 'country_code': the country code in 2 digits
• 'year': the projection year
• 'month': month of the year
• 'day': day of the month
• 'S0_RCP26_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S0_RCP26_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S0_RCP45_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S0_RCP45_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S0_RCP85_r1': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S0_RCP85_r2': time series data for electricity demand corresponding to Scenario S0 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S1_RCP26_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S1_RCP26_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S1_RCP45_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S1_RCP45_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S1_RCP85_r1': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S1_RCP85_r2': time series data for electricity demand corresponding to Scenario S1 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S2_RCP26_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S2_RCP26_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S2_RCP45_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S2_RCP45_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S2_RCP85_r1': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S2_RCP85_r2': time series data for electricity demand corresponding to Scenario S2 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S3_RCP26_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S3_RCP26_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S3_RCP45_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S3_RCP45_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S3_RCP85_r1': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S3_RCP85_r2': time series data for electricity demand corresponding to Scenario S3 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S4_RCP26_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S4_RCP26_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 2.6.
• 'S4_RCP45_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S4_RCP45_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r2, within the context of the Representative Concentration Pathway (RCP) 4.5.
• 'S4_RCP85_r1': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.
• 'S4_RCP85_r2': time series data for electricity demand corresponding to Scenario S4 under the climate model ensemble realization r1, within the context of the Representative Concentration Pathway (RCP) 8.5.

The Europe Battery Energy Storage System Market size was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, exhibiting a CAGR of 1.67 % during the forecasts periods.The European market for BESS is energetic, impelled by ambitious renewable energy targets and modernization initiatives of grids with the support of government policies. In adopting BESS technologies for incorporating increasing wind, solar, and other sources of renewable energies into its electricity grids, the EU's primary constituents are Germany, the UK, France, Italy, and Spain. These systems are very critical to grid stabilization, the management of intermittency, and the supply of reliable energy during peak demand periods. Ambitious environmental legislation to reduce CO2 emissions and increase energy efficiency are key drivers for the European BESS market. Initiatives such as the Clean Energy for All Europeans package of the European Union and national renewable energy targets, set by policy, drive investment into energy storage infrastructure. Equally important, rapid progress in battery development, primarily lithium-ion, which greatly lowers their cost while increasing performance, has made BESS more economical for utilities and companies. Countries like Germany and the UK have also deployed large BESS systems to achieve grid balancing, frequency regulation, and energy arbitrage. Further out, growing EV charging infrastructure and an increased use of BESS to enable EV charging stations are increasing grid flexibility that will underpin the electrification of transport. The outlook for the European BESS market looks bright in the future, with growing investments in renewable energy and energy storage technologies. Recent developments include: February 2023: Power & Air Solutions, the Deutsche Telekom subsidiary, completed its first battery energy storage system (BESS) installation supplied by Pixii. The storage system was installed at one of Deutsche Telekom's main offices in Munich, Germany. The installation in Munich has 1 MW of conversion capacity and 6 MWh of storage capacity., November 2022: RWE announced plans to build a storage facility to provide grid-balancing services for its power plants in Germany. The company aimed to install a total of 220 MW of battery storage capacity at two RWE power plants in North Rhine-Westphalia for about USD 139.9 million. Construction was scheduled for 2023, and operations are expected to commence in 2024. The Hamm, Germany, site will have 140 MW of battery power. It was expected to occupy an area of 14,000 square meters. In Neurath, another facility would have 80 MW of power and occupy an area of 7,000 square meters.. Key drivers for this market are: 4., Increase in Adoption of Renewable Energy 4.; Declining Cost of Lithium Ion Battery. Potential restraints include: 4., Shift Towards Other Energy Storage Systems. Notable trends are: Lithium-ion Segment Expected to Dominate the Market.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

• CORINE Land Cover (CLC) database
• Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/
• Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

• When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

• Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

• Where that data or information has been adapted or modified, the user shall clearly state this.

• The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

• https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

• World exclusive economic zones (EEZ)
• Source: http://www.marineregions.org/sources.php#unioneezcountry
• Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

• http://www.marineregions.org/disclaimer.php

natura/*

• Natura 2000 natural protection areas
• Source: https://www.eea.europa.eu/data-and-maps/data/natura-10
• Extract from Terms of Use:

EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: Directorate-General for Environment (DG ENV).

• https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

naturalearth/*

• World country shapes
• Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/
• Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

• http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

• Europe NUTS3 regions
• Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

1. The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

2. The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

• https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• https://ec.europa.eu/eurostat/about/policies/copyright

gebco/GEBCO_2014_2D.nc

• GEBCO bathymetric dataset
• Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/
• Extract from Terms of Use:

The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid indicates that the user accepts the conditions of use and disclaimer information given below.

Users are free to:

• Copy, publish, distribute and transmit The GEBCO Grid

• Adapt The GEBCO Grid

• Commercially exploit The GEBCO Grid, by, for example, combining it with other information, or by including it in their own product or application

Users must:

• Acknowledge the source of The GEBCO Grid. A suitable form of attribution is given in the documentation that accompanies The GEBCO Grid.

• Not use The GEBCO Grid in a way that suggests any official status or that GEBCO, or the IHO or IOC, endorses any particular application of The GEBCO Grid.

• Not mislead others or misrepresent The GEBCO Grid or its source.

• https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

• Population and GDP data for Swiss Cantons
• Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html
• Extract from Terms of Use:

Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.

Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.

Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given.

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.

The European Automated Demand Response (ADR) market is experiencing robust growth, driven by increasing energy costs, stringent environmental regulations, and the growing adoption of smart grids. The market, valued at approximately €1.5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) exceeding 2% through 2033. This growth is fueled by several key factors. Firstly, the increasing integration of renewable energy sources, such as solar and wind power, necessitates flexible demand-side management solutions to address intermittency. Secondly, governments across Europe are actively promoting ADR through financial incentives and regulatory frameworks aimed at improving energy efficiency and grid stability. Thirdly, technological advancements, particularly in the Internet of Things (IoT) and Artificial Intelligence (AI), are enabling more sophisticated and cost-effective ADR systems. While the market faces some challenges, such as initial investment costs and cybersecurity concerns, these are being mitigated by technological improvements and public-private partnerships. The market segmentation reveals significant opportunities across various segments. Production analysis indicates a strong focus on advanced metering infrastructure (AMI) and intelligent energy management systems. Consumption analysis reveals high demand across commercial and industrial sectors due to their higher energy consumption and potential cost savings. Import and export analyses will reveal regional trade patterns and variations in system adoption rates across different countries. Price trend analysis suggests a downward trend in ADR system costs as technology matures and economies of scale are achieved. Key players like Itron, Hitachi, and Siemens are actively involved in driving market innovation through continuous product development and strategic partnerships. Regional analysis shows strong growth potential in the UK, France, and Germany, driven by their mature energy markets and commitment to sustainable energy solutions. The "Rest of Europe" segment also presents significant opportunities as awareness and adoption of ADR technologies increase. Recent developments include: In December 2021, EU's Joint Research Centre reviewed smart grid projects funded by the European Union under its last two FP7 and Horizon 2020 research funding programmes, as well as its competitiveness and innovation programmes on ICT and intelligent energy. Of the total EUR 3.08 billion invested in the projects, the European Union contribution amounted to EUR 2.32 billion serving as a significant driver., In September 2021, E.on SE acquired a majority stake in Aachen-based smart grid solutions provider GridX GmbH as part of efforts to expand its portfolio of digital solutions for the transition to green energy., In November 2021, In Poland, utility company TAURON has secured a PLN 2.8 billion loan from the European Investment Bank (EIB) to modernise its electricity distribution assets and install an advanced metering infrastructure. The advanced metering infrastructure and smart grid technologies to be installed is expected to enable TAURON to leverage real-time data and analytics to enhance the operations of its grid and customer services.. Notable trends are: Increased Adoption of Smart Grid Technologies.

Europe's Power Supply market will be USD 10510.04 million in 2025 and will grow at a compound annual growth rate (CAGR) of 4.1% from 2025 to 2033. Rising demand for renewable energy integration is expected to aid the sales to USD 14457.2 million by 2033.

Europe Smart Meter Market size was valued at USD 3.76 Billion in 2024 and is projected to reach USD 6.65 Billion by 2032, growing at a CAGR of 7.4% during the forecast period 2026-2032.

Europe Smart Meter Market Drivers

Regulatory Mandates and Government Initiatives: Government policies and regulations are the most significant drivers of the smart meter market in Europe. The European Union’s (EU) ambitious energy policies, such as the Clean Energy Package and the Energy Efficiency Directive, emphasize the adoption of smart meters to promote energy efficiency, reduce carbon emissions, and empower consumers. According to the EU directives, member states are required to replace at least 80% of their conventional meters with smart meters by 2025.

Many European countries have implemented large-scale smart meter rollout programs backed by regulatory frameworks. For example, the UK's Smart Metering Implementation Programme (SMIP) aims to provide smart meters to all residential and small business customers, driving market growth.

Rising Need for Energy Efficiency and Demand Management: Europe's increasing focus on reducing energy waste and optimizing electricity consumption is propelling the demand for smart meters. Smart meters enable consumers to monitor real-time energy usage, make informed decisions about energy consumption, and reduce overall costs. These devices also help utility providers analyze energy demand patterns, prevent grid overloads, and implement effective demand response strategies.

With the growing integration of renewable energy sources, such as wind and solar power, into the grid, there is a greater need for smart meters to ensure grid stability and balance energy demand and supply effectively.

This dataset contains the projections of future freshwater demands of the energy sector (primary energy supply and transformation in power plants and oil refineries) in the EU and the United Kingdom for the period 2015-2050, in 5-year steps. The projections are estimated by the combination of water withdrawal and consumption factors for different energy technologies under six energy scenarios, at national and NUTS2 level, as described in the JRC technical report: Hidalgo Gonzalez, I., Medarac, H. and Magagna, D., Projected freshwater needs of the energy sector in the European Union and the UK, EUR 30266 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-19829-1 (online), doi:10.2760/796885 (online), JRC121030.