In the third quarter of 2025, Bermuda had the highest household electricity prices worldwide, followed by Ireland, Italy, and Germany. At the time, Irish households were charged around 0.44 U.S. dollars per kilowatt-hour, while in Italy, the price stood at 0.42 U.S. dollars per kilowatt-hour. By comparison, in Russia, residents paid almost 10 times less. What is behind electricity prices? Electricity prices vary widely across the world and sometimes even within a country itself, depending on factors like infrastructure, geography, and politically determined taxes and levies. For example, in Denmark, Belgium, and Sweden, taxes constitute a significant portion of residential end-user electricity prices. Reliance on fossil fuel imports Meanwhile, thanks to their great crude oil and natural gas production output, countries like Iran, Qatar, and Russia enjoy some of the cheapest electricity prices in the world. Here, the average household pays less than 0.1 U.S. dollars per kilowatt-hour. In contrast, countries heavily reliant on fossil fuel imports for electricity generation are more vulnerable to market price fluctuations.

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

Household electricity prices vary significantly around the world. In March 2025, the price of electricity was below 0.1 U.S. dollars per kilowatt-hour in countries which rely on nationally produced fossil fuels for electricity generation, while it exceeded 0.4 U.S. dollars per kilowatt-hour where the power sector is dependent on energy imports.

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.

In 2024, Germany recorded the highest residential electricity price for users within Europe, at 44.11 euro cents per kilowatt-hour. This was followed by Ireland, at 36.11 euro cents per kilowatt-hour. Italy, Cyprus, Belgium, Denmark and Czechia were also among the countries with the highest electricity costs.

In 2023, the United Kingdom had the highest industry electricity price worldwide, reaching 25.46 pence per kilowatt-hour. By comparison, the Norway had the lowest price among the listed countries, at 5.31 pence per kilowatt-hour.

Complete dataset of average residential and commercial electricity rates in cents per kWh for all 50 states and D.C. as of December 2025.

In September 2024, industrial electricity prices in the European countries of Germany, Italy, and the United Kingdom were among the highest in the world, at around **** U.S. dollars per kilowatt-hour. Singapore was the Asian country with the highest electricity bill worldwide at that time. Lowest electricity prices in the world The average retail electricity price in the United States was considerably lower than in most of Europe. Iceland was the European country with one of the lowest electricity bills for enterprises that month. At the bottom of the ranking were also Russia, Iraq, Qatar, Argentina, and Libya. In these countries, commercial electricity prices amounted to less than *** U.S. dollars per kilowatt-hour. Household electricity prices In addition, European countries had the highest household electricity prices worldwide that month, with Italy at the top of the ranking. By comparison, Iran and Ethiopia had the lowest residential electricity prices in the world.

US Electricity Prices and Sales by State, Sector, and Year

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2Fdba0dac3571f37e79f2891a6ffd80d5c%2Fus%20electric%20flag.png?generation=1712518711362350&alt=media" alt=""> This comprehensive dataset offers a detailed look at the United States electricity market, providing valuable insights into prices, sales, and revenue across various states, sectors, and years. With data spanning from 2001 onwards to 2024, this dataset is a powerful tool for analyzing the complex dynamics of the US electricity market and understanding how it has evolved over time.

The dataset includes eight key variables: | Column Name | Description | |-------|-------| | year | The year of the observation | | month | The month of the observation | | stateDescription | The name of the state | | sectorName | The sector of the electricity market (residential, commercial, industrial, other, or all sectors) | | customers | The number of customers (missing for some observations) | | price | The average price of electricity per kilowatt-hour (kWh) in cents | | revenue | The total revenue generated from electricity sales in millions of dollars | | sales | The total electricity sales in millions of kilowatt-hours (kWh) |

By providing such granular data, this dataset enables users to conduct in-depth analyses of electricity market trends, comparing prices and consumption patterns across different states and sectors, and examining the impact of seasonality on demand and prices.

One of the primary applications of this dataset is in forecasting future electricity prices and sales based on historical trends. By leveraging the extensive time series data available, researchers and analysts can develop sophisticated models to predict how prices and demand may change in the coming years, taking into account factors such as economic growth, population shifts, and policy changes. This predictive power is invaluable for policymakers, energy companies, and investors looking to make informed decisions in the rapidly evolving electricity market.

Another key use case for this dataset is in investigating the complex relationships between electricity prices, sales volumes, and revenue. By combining the price, sales, and revenue data, users can explore how changes in prices impact consumer behavior and utility company bottom lines. This analysis can shed light on important questions such as the price elasticity of electricity demand, the effectiveness of energy efficiency programs, and the potential impact of new technologies like renewable energy and energy storage on the market.

Beyond its immediate applications in the energy sector, this dataset also has broader implications for understanding the US economy and society as a whole. Electricity is a critical input for businesses and households across the country, and changes in electricity prices and consumption can have far-reaching effects on economic growth, competitiveness, and quality of life. By providing such a rich and detailed portrait of the US electricity market, this dataset opens up new avenues for research and insights that can inform public policy, business strategy, and academic inquiry.

I hope you all enjoy using this dataset and find it useful! 🤗

Graph and download economic data for Average Price: Electricity per Kilowatt-Hour in U.S. City Average (APU000072610) from Nov 1978 to Sep 2025 about electricity, energy, retail, price, and USA.

As of the second quarter of 2025, Australia had the highest average electricity price for households in the Asia-Pacific region, at about **** U.S. dollars per kilowatt-hour. Singapore had the second-highest average electricity price, with households paying around **** U.S. dollars per kilowatt-hour.

Graph and download economic data for Global price of Energy index (PNRGINDEXM) from Jan 1992 to Jun 2025 about energy, World, indexes, and price.

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

Abstract

Energy price inflation is an important metric to inform economic policy but traditional sources of consumer prices are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual price trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes energy price estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Badakhshan, Badghis, Baghlan, Balkh, Bamyan, Daykundi, Farah, Faryab, Paktya, Ghazni, Ghor, Hilmand, Hirat, Nangarhar, Jawzjan, Kabul, Kandahar, Kapisa, Khost, Kunar, Kunduz, Laghman, Logar, Wardak, Nimroz, Nuristan, Paktika, Panjsher, Parwan, Samangan, Sar-e-pul, Takhar, Uruzgan, Zabul, Market Average, Armavir, Ararat, Aragatsotn, Tavush, Gegharkunik, Shirak, Kotayk, Syunik, Lori, Vayotz Dzor, Yerevan, Kanifing Municipal Council, Central River, Upper River, West Coast, North Bank, Lower River, Bafata, Tombali, Cacheu, Sector Autonomo De Bissau, Biombo, Oio, Gabu, Bolama, Quinara, Anbar, Babil, Baghdad, Basrah, Diyala, Dahuk, Erbil, Ninewa, Kerbala, Kirkuk, Missan, Muthanna, Najaf, Qadissiya, Salah al-Din, Sulaymaniyah, Thi-Qar, Wassit, Attapeu, Louangnamtha, Champasack, Bokeo, Bolikhamxai, Khammouan, Oudomxai, Phongsaly, Vientiane, Xiengkhouang, Louangphabang, Salavan, Savannakhet, Sekong, Vientiane Capital, Houaphan, Xaignabouly, Akkar, Mount Lebanon, Baalbek-El Hermel, North, Beirut, Bekaa, El Nabatieh, South, Nimba, Grand Kru, Grand Cape Mount, Gbarpolu, Grand Bassa, Rivercess, Montserrado, River Gee, Lofa, Bomi, Bong, Sinoe, Maryland, Margibi, Grand Gedeh, Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Adamawa, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Shabelle Hoose, Juba Hoose, Bay, Banadir, Shabelle Dhexe, Gedo, Hiraan, Woqooyi Galbeed, Awdal, Bari, Juba Dhexe, Togdheer, Nugaal, Galgaduud, Bakool, Sanaag, Mudug, Sool, , Warrap, Unity, Jonglei, Northern Bahr el Ghazal, Upper Nile, Eastern Equatoria, Central Equatoria, Western Bahr el Ghazal, Western Equatoria, Lakes, Aleppo, Dar'a, Quneitra, Homs, Deir-ez-Zor, Damascus, Ar-Raqqa, Al-Hasakeh, Hama, As-Sweida, Rural Damascus, Tartous, Idleb, Lattakia, Al Dhale'e, Aden, Al Bayda, Al Maharah, Lahj, Al Jawf, Raymah, Al Hudaydah, Hajjah, Amran, Shabwah, Dhamar, Ibb, Sana'a, Al Mahwit, Marib, Hadramaut, Sa'ada, Amanat Al Asimah, Socotra, Taizz, Abyan

Dataset comprising data from five day-ahead electricity markets:

Nord pool: The Nord pool day-ahead electricity market, one of the largest European power market. PJM: The zonal prices of the COMED area in the Pennsylvania-New Jersey-Maryland (PJM) market. EPEX-FR: The French day-ahead electricity market. EPEX-BE: The Belgian day-ahead electricity market. EPEX-DE: The German day-ahead electricity market. Each market contains 6 years of data (we consider a year to be 364 days to have an integer number of weeks). The specific dates are:

Nord pool: 01.01.2013 – 24.12.2018 PJM: 01.01.2013 – 24.12.2018 EPEX-FR: 09.01.2011 – 31.12.2016 EPEX-BE: 09.01.2011 – 31.12.2016 EPEX-DE: 09.01.2012 – 31.12.2017 Each dataset comprises historical prices and two relevant exogenous inputs based on day-ahead forecasts of price drivers. The day--ahead forecast representing other exogenous inputs are market dependent:

Nord pool: System load + Wind power generation. PJM: System load + Zonal load in the COMED area. EPEX-FR: System load + Generation in France EPEX-BE: System load in France + Generation in France EPEX-DE: Zonal load in the TSO Amprion zone + Aggregated Wind and Solar power generation All datasets are given using the local timezone:

Nord pool: Central European Time (CET) PJM: Eastern Time (ET) EPEX-FR: Central European Time (CET) EPEX-BE: Central European Time (CET) EPEX-DE: Central European Time (CET) For all five datasets, the daylight saving times (DST) are pre-processed by interpolating the missing values in Spring and averaging the values corresponding to the duplicated time indices in Autumn.

DISCLAIMER

We do not own the data, but we simply have gathered it so other researchers can easily test their methods on multiple day-ahead markets. The data has been gathered using the respective websites of each day-ahead market where these data are freely available. The websites we used to gather the data are:

Nord Pool: Nord pool website PJM: PJM website EPEX-FR: ENTSO-E transparency platform + RTE website (French TSO) EPEX-BE: ENTSO-E transparency platform + RTE website (French TSO) + Elia website (Belgian TSO) EPEX-DE: ENTSO-E transparency platform + Amprion TSO website + TenneT website + 50Hertz website

Middle Eastern and African countries had the cheapest electricity prices worldwide in December 2024. Namely, the electricity price for Iran's households was only 0.4 U.S. cents per kilowatt-hour of electricity. Ethiopia, Syria, Cuba, and Sudan also had some of the lowest electricity prices worldwide that month.

Comparison of average electricity prices for residential and industrial use in various countries

Japan JP: Residential Electricity Price: USD per kWh data was reported at 0.330 USD/kWh in 2022. This records an increase from the previous number of 0.260 USD/kWh for 2021. Japan JP: Residential Electricity Price: USD per kWh data is updated yearly, averaging 0.220 USD/kWh from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 0.330 USD/kWh in 2022 and a record low of 0.190 USD/kWh in 2002. Japan JP: Residential Electricity Price: USD per kWh data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.GGI: Environmental: Environmental Policy, Taxes and Transfers: OECD Member: Annual.