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.

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! 🤗

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.

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.

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.

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

The global energy price index stood at around 101.5 in 2024. Energy prices were on a decreasing trend that year, and forecasts suggest the price index would decrease below 80 by 2026. Price indices show the development of prices for goods or services over time relative to a base year. Commodity prices may be dependent on various factors, from supply and demand to overall economic growth. Electricity prices around the world As with overall fuel prices, electricity costs for end users are dependent on power infrastructure, technology type, domestic production, and governmental levies and taxes. Generally, electricity prices are lower in countries with great coal and gas resources, as those have historically been the main sources for electricity generation. This is one of the reasons why electricity prices are lowest in resource-rich countries such as Iran, Qatar, and Russia. Meanwhile, many European governments that have introduced renewable surcharges to support the deployment of solar and wind power and are at the same time dependent on fossil fuel imports, have the highest household electricity prices. Benchmark oil prices One of the commodities found within the energy market is oil. Oil is the main raw material for all common motor fuels, from gasoline to kerosene. In resource-poor and remote regions such as the United States' states of Alaska and Hawaii, or the European country of Cyprus, it is also one of the largest sources for electricity generation. Benchmark oil prices such as Europe’s Brent, the U.S.' WTI, or the OPEC basket are often used as indicators for the overall energy price development.

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

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

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.

The dataset contains the evolution of electricity prices in Spain for the period 2020-11-01 to 2022-10-31. The average energy prices (in MWh) as a function of the market, the produced energy (in MW), as well as the renewable energy produced (in MW) by type (wind, solar, hydroelectric, etc.) are provided with a granularity of hours for the period of time mentioned above.

The data has been obtained from the webpage: https://www.esios.ree.es/es/.

Disclaimer: Express consent was provided by Red Eléctrica de España (source and proprietary of the data) to gather the data using web scraping under the framework of a practicum from the Master in Data Science from the Universitat Oberta de Catalunya (UOC). Under no circumstance do they support the reuse of the data. We express our intention to use the data for the purpose of the activity and decline any commercial interest in the use of the data extracted.

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.

UK Gas fell to 72.60 GBp/thm on December 2, 2025, down 1.67% from the previous day. Over the past month, UK Gas's price has fallen 11.75%, and is down 40.33% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. UK Natural Gas - values, historical data, forecasts and news - updated on December of 2025.

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.

New York Energy Prices presents retail energy price data. Energy prices are provided by fuel type in nominal dollars per specified physical unit for the residential, commercial, industrial, and transportation sectors. This section includes a column in the price table displaying gross domestic product (GDP) price deflators for converting nominal (current year) dollars to constant (real) dollars. To convert nominal to constant dollars, divide the nominal energy price by the GDP price deflator for that particular year. Historical petroleum, electricity, coal, and natural gas prices were compiled primarily from the Energy Information Administration. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov.

Natural gas rose to 4.94 USD/MMBtu on December 3, 2025, up 2.04% from the previous day. Over the past month, Natural gas's price has risen 13.71%, and is up 62.29% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Natural gas - values, historical data, forecasts and news - updated on December of 2025.

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.

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.

It is presented two datasets used to train a neural network that forecasts electricity prices in the Yucatan peninsula. The first one is the Input data, which is composed of five parameters, three describing environmental conditions and two reporting the levels of operation of the electricity system in the study region. The second is the output data, corresponding to local marginal electricity prices. These prices are compound from the next three costs: energy, losses of transmission, and congestion.

Also, these data allow detecting the dynamics of the electricity market, which can be related to environmental conditions. Also, they allow detecting phenomena of the electricity market, i.e. negative prices of transmission losses or congestion, and the negative merit-order effect.

Every parameter was collected for eight load zones in hourly resolution, it is the geographic distribution according to the Mexican independent system operator. The data begins in the first hour of January 1st of 2017 and ends in the last hour of April 4th of 2019. Each parameter has 157808 observations.

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