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.

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! 🤗

In 2022, the average end-use electricity price in the United States stood at around 12.2 U.S. cents per kilowatt-hour. This figure is projected to decrease in the coming three decades, to reach some 11 U.S. cents per kilowatt-hour by 2050.

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.

Wholesale electricity prices in the United Kingdom hit a record-high in 2022, reaching **** British pence per kilowatt-hour that year. Projections indicate that prices are bound to decrease steadily in the next few years, falling under **** pence per kilowatt-hour by 2030.

Electricity prices in Germany are forecast to amount to ***** euros per megawatt-hour in November 2025. Electricity prices in the country have not yet recovered to pre-pandemic levels. Electricity price recovery German electricity prices began recovering back to pre-energy crisis levels in 2024, a period driven by a complex interplay of factors, including increased heating demand, reduced wind power generation, and water scarcity affecting hydropower production. Despite Germany's progress in renewable energy sources, with over ** percent of gross electricity generated from renewable sources in 2024, the country still relies heavily on fossil fuels. Coal and natural gas accounted for approximately ** percent of the energy mix, making Germany vulnerable to fluctuations in global fuel prices. Impact on consumers and future outlook The volatility in electricity prices has directly impacted German consumers. As of April 1, 2024, households with basic supplier contracts were paying around ** cents per kilowatt-hour, making it the most expensive option compared to other providers or special contracts. The breakdown of household electricity prices in 2023 showed that supply and margin, along with energy procurement, constituted the largest controllable components, amounting to **** and **** euro cents per kilowatt-hour, respectively. While prices have decreased since the 2022 peak, they remain higher than pre-crisis levels, underscoring the ongoing challenges in Germany's energy sector as it continues its transition towards renewable sources.

UK Electricity decreased 23.24 GBP/MWh or 22.68% 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 the United Kingdom Electricity Price.

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

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.

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.

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.

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...

Electricity Market Transparency

​Transparency has improved markedly in Europe over the past few years, culminating in Regulation (EU) No 5 43/2013 of 14 June 2013 on submission and publication of data in electricity markets. Through this Regulation, it has now become mandatory for European Member State data providers and owners to submit fundamental information related to electricity generation, load, transmission and balancing for publication through the ENTSO-E Transparency Platform.

Transparency is essential for the implementation of the Internal Electricity Market (IEM) and for the creation of efficient, liquid and competitive wholesale markets. It is also critical for creating a level playing field between market participants and avoiding the scope for market power (if it exists) to be abused.This platform enables the provision of the required electricity market information for the future and further facilitates the development of efficient and competitive energy markets across Europe. Such developments support the steady evolution of electricity markets across Europe in terms of integration, competition

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Wind Speed's Impact on Spanish Power Prices

A Study of Market Efficiency

By [source]

About this dataset

This dataset provides a comprehensive analysis of the influence of wind speeds on short-term electricity prices in the Spanish electricity market, OMIE. It includes information on average, minimum and maximum daily power prices in euros per megawatt hour (€/MWh) along with corresponding data from observational points about wind speed and strong gusts in kilometres per hour (km/h).

By exploring the interactions between weather patterns and energy markets, this dataset is a valuable tool for energy stakeholders looking to forecast and manage their prices more effectively. It’s also an important resource for scientists, weather agencies and environmental regulators who need to get a handle on how changing wind patterns can impact pricing in the short term. Finally, this data is ideal for educational use as well – providing an insightful overview of how external factors can influence power costs

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How to use the dataset

This dataset is useful to identify the influence of wind speed observations on the power prices in the Spanish electricity market, OMIE. By understanding this relationship, stakeholders can develop strategies to forecast, manage and optimize energy production and consumption.

To make use of this dataset one should begin by exploring the data with visualizations and summary statistics. This will provide an overview of the average daily prices in euros per megawatt hour (€/MWh) as well as associated temperatures obtained by a series of wind data observation points in kilometres per hour (km/h). Comparing these variables will allow for analysis into their correlations and any seasonal fluctuations present. Additionally, further exploration can be made by plotting multiple variables against each other such as maximum power prices and percentage of maximum wind speeds achieved over various timeframes.

Once the individual components are better understood, more comprehensive assessment can be conducted including linear regression models to evaluate interaction between independent variablen like hourly temperature observations and dependent variables like price fluctuations due to variability in demand or supply availability within given hours or days etc. With this knowledge refined analysis can be done not only with current data but future predictions from driving forces within market trends etc along with relevant external factors such as weather patterns etc too if needed could also be studied using correlation or causality studies using advanced modelling techniques if required

Research Ideas

• Developing pricing models and strategies in the energy market by analyzing the correlation between wind speeds and power prices across different time periods compared to various influencing factors such as supply, demand, weather conditions etc.
• Utilizing this data to develop concepts and strategies for forecasting electricity prices with much higher accuracy than traditional methods .
• Exploring the impacts of wind farm construction on the voltage stability and long-term price trends in regional electric grids by studying how new wind farms affect the regional power mix mix and corresponding supply/demand curves over time

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: wind_vs_price.csv | Column name | Description | |:---------------------------|:------------------------------------------------------------------------| | fecha | Date of the observation. (Date) | | MIN(dp.precio) | Minimum daily power price in euros per megawatt hour (€/MWh). (Numeric) | | AVG(dp.precio) | Average daily power price in euros per megawatt hour (€/MWh). (Numeric) | | MAX(dp.precio) | Maximum daily power price in euros per megawatt hour (€/MWh). (Numeric) | | AVG(wd.vel_km_h) | Average wind speed in kilometres per hour (...

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.

Given the key role of renewable energies in current and future electricity markets, it is important to understand how they affect firms' pricing incentives in these markets. In this paper, we study whether renewables depress electricity market prices, and how this effect depends on their degree of market price exposure. Our theoretical analysis shows that paying renewables with fixed prices, rather than with market-based prices, is relatively more effective at curbing market power when the dominant electricity firms own large shares of the renewable capacity, and vice-versa. To test this prediction, our empirical analysis leverages several short-lived changes to renewable energy pricing mechanisms in the Spanish electricity market. In this context, we find that the switch from full price exposure to fixed prices caused a 2-4% reduction in the average price-cost markup.

California Grid Hourly Prices

2009 Electricity Prices and Net Load Data

By [source]

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 .

According to a recent forecast, industrial electricity prices in Europe in 2030 will be lowest in Germany if an electricity price compensation for companies is enacted. France will account for the second-lowest electricity price for enterprises if the ARENH tariff program is maintained. In the ARENH program, businesses have access to nuclear power at a regulated tariff.

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.