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! 🤗

Annual data on the average price of retail electricity to consumers. Data organized by U.S. state and by provider, i.e., total electric industry, full-service providers, restructured retail service providers, energy-only providers, and delivery-only service. Annual time series extend back to 1990. Based on Form EIA-861 data.

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.

Daily data showing the System Price of electricity, and rolling seven-day average, in Great Britain. These are official statistics in development. Source: Elexon.

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 contains sector level energy prices for 12 industrial sectors in 48 countries for the period 1995 to 2015. The prices are constructed as weighted averages of fuel-specific prices by fuel consumption. Two industrial energy price measures have been developed: the Variable Weights Energy Price Level (VEPL) and the Fixed Weights Energy Price Index (FEPI). Original data are provided by the International Energy Agency, as well as other sources. The procedures used to construct the dataset including the methodology developed to reduce missing data-points, are documented in the accompanying paper (link below). We also provide guidelines on how to use the energy price data along with a ready made instrumental variable, as well as a set of stylized facts on major trends and variations, and illustrative applications.

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.

California Grid Hourly Prices

2009 Electricity Prices and Net Load Data

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About this dataset

This dataset offers a comprehensive examination of hourly energy prices and net load for California during 2009. Accessed via HiGRID, this dataset contains detailed information such as the day, hour, net load ([MW]), and electricity price ([$/MWh]) to provide users with an insightful view of the energy consumption in the region throughout the year. By understanding these prominent figures of electricity use, users can develop economically savvy solutions to reduce their energy costs while living sustainably

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How to use the dataset

This dataset contains hourly electricity prices and net load data for California in 2009. It is intended to be used as input for modeling energy-efficiency in buildings.

Here’s how you can use this dataset to model the energy efficiency of a building: - Gain an understanding of the current net load in your area (Net Load [MW]). Net load refers to the total amount of electricity used by all customers minus the total amount generated from power plants and other sources. It’s important to understand current conditions since they will affect your building’s power consumption and future bills. 2 Examine day-of-week trends in energy usage (Day). Studying these trends will help you predict when peak demand occurs, as well as when pricing may increase or decrease due to changes in consumer behavior.
3 Analyze hourly levels of electricity price (Electricity Price [$/MWh]). Knowing what time each day is more expensive than others allows you to adjust building behaviors accordingly, such as using more efficient equipment during peak hours or implementing strategies like storage or load shifting that take advantage of any price arbitrage opportunities between different times blocks during certain days of the week . 4 Review overall average costs over a long period of time (Hour). Comparing month-to-month values for both net load and prices helps ensure that planned improvements are creating real cost savings results over time, especially when benchmarked against previous normal operating conditions observed over a long period giving reliable normalized baseline accuracy with less variability analysis than any individual data set could provide from within its respective domain's sample space alone

Research Ideas

• Analyzing the correlation between electricity prices and net load in order to identify optimal times for businesses to purchase and use electricity.
• Assessing the impact of different external factors (e.g., weather) on energy prices and net load in order to inform decision making on energy strategy and investment opportunities.
• Utilizing time-series data analytics to study patterns in net load across days of the week, as well as within specified time frames (e.g., peak hours) over larger periods of time, such as months or years

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Historical_Net_Load_and_Electricity_Price.csv | Column name | Description | |:------------------------------|:-----------------------------------------------------------| | Day | The day of the week. (String) | | Hour | The hour of the day. (Integer) | | Net Load [MW] | The amount of electricity being used in megawatts. (Float) | | Electricity Price [$/MWh] | The cost of electricity per megawatt hour. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

A dataset of fixed-rate business electricity plans available in Texas for November 2025. Data includes provider, contract length, and price per kilowatt-hour.

Predicting The Energy Market's Day Ahead Prices

Analyses of Energy Systems and Prices

By [source]

About this dataset

This dataset offers an in-depth look at the dynamic European energy markets, with hourly updates on the power prices associated with each system. By offering a comprehensive view of electricity markets across Europe, this data can empower both academics and those in industry to draw implications from correlations between different energy systems, analyze how prices fluctuate across markets, and better understand the complex dynamics of these European energy systems. This comprehensive dataset provides invaluable insights into economic trends in this region and the future outlook for energy pricing

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How to use the dataset

This dataset provides an excellent analysis of Europe’s energy systems and power prices on an hourly basis. It can be used in many ways to examine the electricity market of Europe, including correlations between different energy systems, implications for prices in specific markets, and much more.

Here is a guide for how to use this dataset: - First inspect the columns provided in this dataset; they include date/time information (fecha, hora), system (sistema), flag (bandera), price (precio), currency type (tipo_moneda), source of data(origen_dato) and date of last update(fecha_actualizacion). - Understand what each column represents as some columns may be more important than others depending on your particular analysis. For example, when examining energy system correlations you may want to focus primarily on the ‘system’ column while if price fluctuations are your focus you may want to pay most attention to the ‘price’ column. - Gather the data from all desired columns that you need for your analysis into a single table or format for better organization and readability. This will make it easier to visualize trends or patterns that you find interesting.
- Utilize tools such as Microsoft Excel functions or programming languages such as Python/R to create representations like line graphs which reveal correlations over time or region-specific market power price differences etc.
- Finally present your findings in written form such as a report or share visualized results like infographics!

Research Ideas

• Analyzing correlations between energy systems in Europe, price behavior and its implications across different markets.
• Analyzing historical trends in pricing behavior to predict future prices for energy markets in Europe.
• Recommending differentiated approaches for infrastructure investments that mitigate risk and optimize cost benefit analysis among utilities and businesses across Europe's electricity markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: da_market_data.csv | Column name | Description | |:------------------------|:--------------------------------------------------------------------------------------------------------------------------------| | fecha | Date of the power prices in DD/MM/YYYY format. (Date) | | hora | Hour that corresponds with each set of power prices listed by minute. (Time) | | sistema | Numeric code for system identifier for each set of reported price points for a specific hour across EU countries. (Numeric) | | bandera | Indicator of whether or not electricity is green (Y) or non-green/conventional electricity (N). (Boolean) | | precio | Cost per Megawatt Hour expressed in Euro €/MWh currency format. (Currency) | | tipo_moneda | Euros represented as Euros € EUROSCURSUSD ($ EURS = US Dollars $ USD) as well as other available foreign currencies. (Currency) | | origen_dato | D...

Pakistan PK: Industry Electricity Price: USD per kWh data was reported at 0.480 USD/kWh in 2020. This records an increase from the previous number of 0.420 USD/kWh for 2019. Pakistan PK: Industry Electricity Price: USD per kWh data is updated yearly, averaging 0.420 USD/kWh from Dec 2014 (Median) to 2020, with 7 observations. The data reached an all-time high of 0.520 USD/kWh in 2014 and a record low of 0.360 USD/kWh in 2018. Pakistan PK: Industry 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 Pakistan – Table PK.OECD.GGI: Environmental: Environmental Policy, Taxes and Transfers: Non OECD Member: Annual.

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.

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.

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

This table shows the average prices paid for natural gas and electricity. The total prices represent the sum of energy supply prices and network prices.

The total price is the price paid by an end-user, for instance a household or an industrial company consuming energy in their production process. Natural gas used for non-energy purposes or for electricity generation is excluded from the data.

Data available from: 1st semester of 2009

Status of the figures: The figures in this table are provisional for the two most recent semesters, and the annual figures follow the status of the second semester of the relevant reporting year. The remaining figures are final.

Changes as of September 30: Figures for the first half of 2025 have been added.

The network prices for final non-household customers will from now on, and dating back to 2009, be derived from administrative data sources. This now follows the methodology for households. Consumption data can be combined with tariffs that are published on the websites of the network companies, providing the necessary data to compile the prices. The change in methodology is carried out for the full time-series, making sure the network prices are consistent and price changes are not the result of varying measurement approaches.

When will new figures be published? New provisional figures will be published three months after the semesters end, at the end of September and at the end of March.

This table contains consumer prices for electricity and gas. Weighted average monthly prices are published broken down into transport rate, delivery rates and taxes, both including and excluding VAT. These prices are published on a monthly basis.

Data available from: January 2021

Status of the figures: When first published, the figures are provisional. These will become definitive with the following month’s publication.

Changes compared with previous version: Data on the most recent period have been added and/or adjustments have been implemented.

When will new figures be published? New figures will usually be published between the first and second Thursday of the month.

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.