Electricity price forecasting (EPF) is a branch of energy forecasting which focuses on predicting the spot and forward prices in wholesale electricity markets. Over the last 15 years electricity price forecasts have become a fundamental input to energy companies’ decision-making mechanisms at the corporate level.

This dataset is a sample of day ahead spotprices in Denmark(DK) and neighboring countries. Prices in DKK are not updated during weekends and on public holidays. Prices in DKK will be updated at the first coming work day.

In Nord Pool Spot market players can buy and sell electricity for delivery the following day in their own area - Norway, Sweden, Finland, Denmark or Germany. The day-ahead prices indicate the balance between supply and demand. Nord Pool

Features :

• Hour UTC: A date and time (interval), shown in UTC time zone, where the values are valid. 00:00 o’clock is the first hour of a given day interval 00:00 - 00:59 and 01:00 covers the second hour (interval) of the day and so forth. Please note: The naming is based on the length of the interval of the finest grain of the resolution.

• Hour DK: A date and time (interval), shown in Danish time zone, where the values are valid. 00:00 o’clock is the first hour of a given day, interval 00:00 - 00:59, and 01:00 covers the second hour period (interval) of the day and so forth.

• Price area: Same as bidding zone. Denmark is divided in two price areas, or bidding zones, divided by the Great Belt. DK1 is west of the Great Belt and DK2 is east of the Great Belt.

• Spot price (DKK): Day ahead Spot Price in the price area(The day-ahead prices indicate the balance between supply and demand.)[Unit: DKK per MWh]

• Spot price (EUR): Day ahead Spot Price in the price area(The day-ahead prices indicate the balance between supply and demand.) [Unit: EUR per MWh]

more: tso-electricity

Inspiration:

• Time-series analysis
• Univariate single and multistep electricity spot price forecasting

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! 🤗

Wholesale Energy Spot Market: Scarcity Price data was reported at 658.630 COP/kWh in 28 Nov 2025. This stayed constant from the previous number of 658.630 COP/kWh for 27 Nov 2025. Wholesale Energy Spot Market: Scarcity Price data is updated daily, averaging 945.060 COP/kWh from Jan 2020 (Median) to 28 Nov 2025, with 2159 observations. The data reached an all-time high of 1,037.230 COP/kWh in 30 Aug 2022 and a record low of 303.840 COP/kWh in 29 Jun 2020. Wholesale Energy Spot Market: Scarcity Price data remains active status in CEIC and is reported by XM Compañía de Expertos en Mercados S.A. E.S.P.. The data is categorized under Global Database’s Colombia – Table CO.P: Wholesale Energy Market Price. The Colombian Energy Exchange, managed by XM, in which generators and traders participate, selling and buying energy at the exchange price, has a ceiling price for the sale of energy called scarcity price, which corresponds to the maximum value that the country's demand can pay for energy. This price is calculated monthly according to the calculations established in the regulation that defines the reliability charge scheme. The scarcity price is determined according to the variable costs associated with the SIN (Sistema Interconectado Nacional) and the price of the fuel

France Electricity decreased 21.25 EUR/MWh or 30.42% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for France Electricity Price.

Germany Electricity decreased 17.60 EUR/MWh or 15.21% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for Germany Electricity Price.

This dataset presents a unified, cross-continental time-series day-ahead electricity prices compiled from major wholesale markets across Asia, Europe, North America, South America, and Oceania. The dataset offers a standardized format that supports time-series forecasting and enables robust comparative analysis across diverse global electricity markets.

The average wholesale electricity price in September 2025 in the United Kingdom is forecast to amount to*******British pounds per megawatt-hour, a decrease from the previous month. A record high was reached in August 2022 when day-ahead baseload contracts averaged ***** British pounds per megawatt-hour. Electricity price stabilization in Europe Electricity prices increased in 2024 compared to the previous year, when prices stabilized after the energy supply shortage. Price spikes were driven by the growing wholesale prices of natural gas and coal worldwide, which are among the main sources of power in the region.

… and in the United Kingdom? The United Kingdom was one of the countries with the highest electricity prices worldwide during the energy crisis. Since then, prices have been stabilizing, almost to pre-energy crisis levels. The use of nuclear, wind, and bioenergy for electricity generation has been increasing recently. The fuel types are an alternative to fossil fuels and are part of the country's power generation plans going into the future.

Dataset Description Title: Electricity Market Dataset for Long-Term Forecasting (2018–2024)

Overview: This dataset provides a comprehensive collection of electricity market data, focusing on long-term forecasting and strategic planning in the energy sector. The data is derived from real-world electricity market records and policy reports from Germany, specifically the Frankfurt region, a major European energy hub. It includes hourly observations spanning from January 1, 2018, to December 31, 2024, covering key economic, environmental, and operational factors that influence electricity market dynamics. This dataset is ideal for predictive modeling tasks such as electricity price forecasting, renewable energy integration planning, and market risk assessment.

Features Description Feature Name Description Type Timestamp The timestamp for each hourly observation. Datetime Historical_Electricity_Prices Hourly historical electricity prices in the Frankfurt market. Continuous (Float) Projected_Electricity_Prices Forecasted electricity prices (short, medium, long term). Continuous (Float) Inflation_Rates Hourly inflation rate trends impacting energy markets. Continuous (Float) GDP_Growth_Rate Hourly GDP growth rate trends for Germany. Continuous (Float) Energy_Market_Demand Hourly electricity demand across all sectors. Continuous (Float) Renewable_Investment_Costs Investment costs (capital and operational) for renewable energy projects. Continuous (Float) Fossil_Fuel_Costs Costs for fossil fuels like coal, oil, and natural gas. Continuous (Float) Electricity_Export_Prices Prices for electricity exports from Germany to neighboring regions. Continuous (Float) Market_Elasticity Sensitivity of electricity demand to price changes. Continuous (Float) Energy_Production_By_Solar Hourly solar energy production. Continuous (Float) Energy_Production_By_Wind Hourly wind energy production. Continuous (Float) Energy_Production_By_Coal Hourly coal-based energy production. Continuous (Float) Energy_Storage_Capacity Available storage capacity (e.g., batteries, pumped hydro). Continuous (Float) GHG_Emissions Hourly greenhouse gas emissions from energy production. Continuous (Float) Renewable_Penetration_Rate Percentage of renewable energy in total energy production. Continuous (Float) Regulatory_Policies Categorical representation of regulatory impact on electricity markets (e.g., Low, Medium, High). Categorical Energy_Access_Data Categorization of energy accessibility (Urban or Rural). Categorical LCOE Levelized Cost of Energy by source. Continuous (Float) ROI Return on investment for energy projects. Continuous (Float) Net_Present_Value Net present value of proposed energy projects. Continuous (Float) Population_Growth Population growth rate trends impacting energy demand. Continuous (Float) Optimal_Energy_Mix Suggested optimal mix of renewable, non-renewable, and nuclear energy. Continuous (Float) Electricity_Price_Forecast Predicted electricity prices based on various factors. Continuous (Float) Project_Risk_Analysis Categorical analysis of project risks (Low, Medium, High). Categorical Investment_Feasibility Indicator of the feasibility of energy investments. Continuous (Float) Use Cases Electricity Price Forecasting: Utilize historical and projected price trends to predict future electricity prices. Project Risk Classification: Categorize projects into risk levels for better decision-making. Optimal Energy Mix Analysis: Analyze the balance between renewable, non-renewable, and nuclear energy sources. Policy Impact Assessment: Study the effect of regulatory and market policies on energy planning. Long-Term Strategic Planning: Provide insights into investment feasibility, GHG emission reduction, and energy market dynamics. Acknowledgment This dataset is based on publicly available records and market data specific to the Frankfurt region, Germany. The dataset is designed for research and educational purposes in energy informatics, computational intelligence, and long-term forecasting.

Italy Electricity decreased 4.07 EUR/MWh or 2.96% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for Italy Electricity Price.

Spain Electricity decreased 65.44 EUR/MWh or 48.17% since the beginning of 2025, according to the latest spot benchmarks offered by sellers to buyers priced in megawatt hour (MWh). This dataset includes a chart with historical data for Spain Electricity Price.

Load, wind, solar power generation and electricity prices in 15 minutes resolution

This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighboring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020.

Field Name : Description

utc_timestamp : Start of timeperiod in Coordinated Universal Time

cet_cest_timestamp : Start of timeperiod in Central European (Summer-) Time

AT_load_actual_entsoe_transparency : Total load in Austria in MW as published on ENTSO-E Transparency Platform

AT_load_forecast_entsoe_transparency : Day-ahead load forecast in Austria in MW as published on ENTSO-E Transparency Platform

AT_solar_generation_actual : Actual solar generation in Austria in MW

AT_wind_onshore_generation_actual : Actual wind_onshore generation in Austria in MW

BE_load_actual_entsoe_transparency : Total load in Belgium in MW as published on ENTSO-E Transparency Platform

BE_load_forecast_entsoe_transparency : Day-ahead load forecast in Belgium in MW as published on ENTSO-E Transparency Platform

DE_load_actual_entsoe_transparency : Total load in Germany in MW as published on ENTSO-E Transparency Platform

DE_load_forecast_entsoe_transparency : Day-ahead load forecast in Germany in MW as published on ENTSO-E Transparency Platform

DE_solar_capacity : Electrical capacity of solar in Germany in MW

DE_solar_generation_actual : Actual solar generation in Germany in MW

DE_solar_profile : Share of solar capacity producing in Germany

DE_wind_capacity : Electrical capacity of wind in Germany in MW

DE_wind_generation_actual : Actual wind generation in Germany in MW

DE_wind_profile : Share of wind capacity producing in Germany

DE_wind_offshore_capacity : Electrical capacity of wind_offshore in Germany in MW

DE_wind_offshore_generation_actual : Actual wind_offshore generation in Germany in MW

DE_wind_offshore_profile : Share of wind_offshore capacity producing in Germany

DE_wind_onshore_capacity : Electrical capacity of wind_onshore in Germany in MW

DE_wind_onshore_generation_actual : Actual wind_onshore generation in Germany in MW

DE_wind_onshore_profile : Share of wind_onshore capacity producing in Germany

DE_50hertz_load_actual_entsoe_transparency : Total load in 50Hertz (control area) in MW as published on ENTSO-E Transparency Platform

DE_50hertz_load_forecast_entsoe_transparency : Day-ahead load forecast in 50Hertz (control area) in MW as published on ENTSO-E Transparency Platform

DE_50hertz_solar_generation_actual : Actual solar generation in 50Hertz (control area) in MW

DE_50hertz_wind_generation_actual : Actual wind generation in 50Hertz (control area) in MW

DE_50hertz_wind_offshore_generation_actual : Actual wind_offshore generation in 50Hertz (control area) in MW

DE_50hertz_wind_onshore_generation_actual : Actual wind_onshore generation in 50Hertz (control area) in MW

DE_amprion_load_actual_entsoe_transparency : Total load in Amprion (control area) in MW as published on ENTSO-E Transparency Platform

DE_amprion_load_forecast_entsoe_transparency : Day-ahead load forecast in Amprion (control area) in MW as published on ENTSO-E Transparency Platform

DE_amprion_solar_generation_actual : Actual solar generation in Amprion (control area) in MW

DE_amprion_wind_onshore_generation_actual : Actual wind_onshore generation in Amprion (control area) in MW

DE_tennet_load_actual_entsoe_transparency : Total load in TenneT GER (control area) in MW as published on ENTSO-E Transparency Platform

DE_tennet_load_forecast_entsoe_transparency : Day-ahead load forecast in TenneT GER (control area) in MW as published on ENTSO-E Transparency Platform

DE_tennet_solar_generation_actual : Actual solar generation in TenneT GER (control area) in MW

DE_tennet_wind_generation_actual : Actual wind generation in TenneT GER (control area) in MW

DE_tennet_wind_offshore_generation_actual : DE_tennet_wind_offshore_generation_actual

DE_tennet_wind_onshore_generation_actual : Actual wind_onshore generation in TenneT GER (control area) in MW

DE_transnetbw_load_actual_entsoe_transparency : Total load in TransnetBW (control area) in MW as published on ENTSO-E Transparency Platform

**DE_transnetbw_load_forecast_entsoe_...

Electricity prices in Europe are expected to remain volatile through 2025, with Italy projected to have some of the highest rates among major European economies. This trend reflects the ongoing challenges in the energy sector, including the transition to renewable sources and the impact of geopolitical events on supply chains. Despite efforts to stabilize the market, prices still have not returned to pre-pandemic levels, such as in countries like Italy, where prices are forecast to reach ****** euros per megawatt hour in September 2025. Natural gas futures shaping electricity costs The electricity market's future trajectory is closely tied to natural gas prices, a key component in power generation. Dutch TTF gas futures, a benchmark for European natural gas prices, are projected to be ***** euros per megawatt hour in July 2025. The reduced output from the Groningen gas field and increased reliance on imports further complicate the pricing landscape, potentially contributing to higher electricity costs in countries like Italy. Regional disparities and global market influences While European electricity prices remain high, significant regional differences persist. For instance, natural gas prices in the United States are expected to be roughly one-third of those in Europe by March 2025, at **** U.S. dollars per million British thermal units. This stark contrast highlights the impact of domestic production capabilities on global natural gas prices. Europe's greater reliance on imports, particularly in the aftermath of geopolitical tensions and the shift away from Russian gas, continues to keep prices elevated compared to more self-sufficient markets. As a result, countries like Italy may face sustained pressure on electricity prices due to their position within the broader European energy market. As of August 2025, electricity prices in Italy have decreased to ****** euros per megawatt hour, reflecting ongoing volatility in the market.

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.

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.

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

In the week starting October 6th, 2025, the lowest day-ahead price of electricity traded on the NordPool spot market was recorded in Norway at **** euros per megawatt-hour. The highest day-ahead price was recorded in Denmark at *******euros per megawatt-hour. The Nordic spot market is divided into sub-regions to balance production and consumption, and avoid congestion of the electricity grid. Only a part of the electricity supplied to Denmark, Sweden, Finland, and Norway is sold on the NordPool market, with the remainder exchanged through long-term or bilateral contracts.

Electricity Average Spot Price: South Australia: Maximum data was reported at 349.090 AUD/MWh in 17 May 2025. This records an increase from the previous number of 314.430 AUD/MWh for 16 May 2025. Electricity Average Spot Price: South Australia: Maximum data is updated daily, averaging 188.615 AUD/MWh from Jan 1999 (Median) to 17 May 2025, with 9634 observations. The data reached an all-time high of 17,500.000 AUD/MWh in 12 Feb 2025 and a record low of -26.870 AUD/MWh in 20 Nov 2022. Electricity Average Spot Price: South Australia: Maximum data remains active status in CEIC and is reported by Australian Energy Market Operator. The data is categorized under Global Database’s Australia – Table AU.P003: Electricity Prices.

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

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.

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: Abia, Borno, Yobe, Katsina, Kano, Kaduna, Gombe, Jigawa, Kebbi, Oyo, Sokoto, Zamfara, Lagos, Adamawa, Market Average