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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! 🤗
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TwitterComplete dataset of average residential and commercial electricity rates in cents per kWh for all 50 states and D.C. as of December 2025.
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TwitterIn 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.
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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.
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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.
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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]
Inspiration:
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TwitterThe 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.
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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.
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TwitterThe retail price for electricity in the United States stood at an average of ***** U.S. dollar cents per kilowatt-hour in 2024. This is the highest figure reported in the indicated period. Nevertheless, the U.S. still has one of the lowest electricity prices worldwide. As a major producer of primary energy, energy prices are lower than in countries that are more reliant on imports or impose higher taxes. Regional variations and sector disparities The impact of rising electricity costs across U.S. states is not uniform. Hawaii stands out with the highest household electricity price, reaching a staggering ***** U.S. cents per kilowatt-hour in September 2024. This stark contrast is primarily due to Hawaii's heavy reliance on imported oil for power generation. On the other hand, states like Utah benefit from lower rates, with prices around **** U.S. cents per kilowatt-hour. Regarding U.S. prices by sector, residential customers have borne the brunt of price increases, paying an average of ***** U.S. cents per kilowatt-hour in 2023, significantly more than commercial and industrial sectors. Factors driving price increases Several factors contribute to the upward trend in electricity prices. The integration of renewable energy sources, investments in smart grid technologies, and rising peak demand all play a role. Additionally, the global energy crisis of 2022 and natural disasters affecting power infrastructure have put pressure on the electric utility industry. The close connection between U.S. electricity prices and natural gas markets also influences rates, as domestic prices are affected by higher-paying international markets. Looking ahead, projections suggest a continued increase in electricity prices, with residential rates expected to grow by *** percent in 2024, driven by factors such as increased demand and the ongoing effects of climate change.
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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.
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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.
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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
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Graph and download economic data for Average Price: Electricity per Kilowatt-Hour in the New England Census Division (APU011072610) from Jan 2018 to Dec 2024 about electricity, energy, retail, price, and USA.
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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.
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TwitterThis API provides data on average retail price of electricity by major end-use sectors, i.e., residential, commercial, industrial, and transportation. Based on Form EIA-826 and Form EIA-861 data. Annual, quarterly, and monthly data available.
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Graph and download economic data for Average Price: Electricity per Kilowatt-Hour in Chicago-Naperville-Elgin, IL-IN-WI (CBSA) (APUS23A72610) from Nov 1978 to Dec 2024 about Chicago, electricity, energy, WI, IN, IL, urban, retail, price, and USA.
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TwitterIn 2024, the retail price of electricity for residential customers in the United States averaged 16.48 U.S. cents per kilowatt-hour. Households are charged more than the commercial and industrial sectors, because of the higher distribution costs. Since 2020, electricity customers have seen electricity prices increase in the U.S. and peak in 2024. The U.S. electricity market The U.S. electricity market is led by several types of electricity providers, such as cooperatives, municipal systems, and shareholder-owned electric utilities. In 2022, cooperatives were the most common type of ownership in the U.S., with more than 600 providers. That year, the U.S. electric utility industry revenue amounted to 488 billion U.S. dollars. Electricity prices around the world Electricity prices vary widely from country to country, depending on energy sources used, as well as government and industry subsidies and regulations. In 2023, Ireland and the United Kingdom had some of the highest household electricity prices worldwide. Meanwhile, U.S. households paid some of the lowest prices. However, leading oil and gas-producing regions such as the Middle East registered the cheapest rates overall.
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Graph and download economic data for Average Price: Electricity per Kilowatt-Hour in Dallas-Fort Worth-Arlington, TX (CBSA) (APUS37A72610) from Nov 1978 to Dec 2024 about Dallas, electricity, energy, urban, TX, retail, price, and USA.
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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.
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TwitterHistorical electricity data series updated annually in July alongside the publication of the Digest of United Kingdom Energy Statistics (DUKES).
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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! 🤗