The Big Data Analytics Market in Energy Sector size was valued at USD 9.56 USD Billion in 2023 and is projected to reach USD 13.81 USD Billion by 2032, exhibiting a CAGR of 5.4 % during the forecast period. Big Data Analytics in the energy sector can be defined as the application of sophisticated methods or tools in analyzing vast collections of information that are produced by numerous entities within the energy industry. This process covers descriptive, predictive, and prescriptive analytics to provide valuable information for procedures, costs, and strategies. Real-time analytics, etc are immediate, while predictive analytics focuses on the probability to happen in the future and prescriptive analytics solutions provide recommendations for action. Some of the main characteristics of the data collectors include handling large datasets, compatibility with IoT to stream data, and machine learning features for pattern detection. These can range from grid control and load management to predicting customer demand and equipment reliability and equipment efficiency enhancement. Thus, there is a significant advantage because Big Data Analytics helps global energy companies to increase performance, minimize sick time, and develop effective strategies to meet the necessary legal demands. Key drivers for this market are: Growing Focus on Safety and Organization to Fuel Market Growth. Potential restraints include: Higher Cost of Geotechnical Services to Hinder Market Growth. Notable trends are: Growth of IT Infrastructure to Bolster the Demand for Modern Cable Tray Management Solutions.

Renewable Energy Machine Learning Dataset Market Outlook

According to our latest research, the global Renewable Energy Machine Learning Dataset market size reached USD 1.28 billion in 2024, reflecting robust momentum driven by the rapid digitalization of the energy sector and increasing reliance on data-driven insights. The market is expected to expand at a remarkable CAGR of 19.6% from 2025 to 2033, ultimately reaching a projected value of USD 6.10 billion by 2033. The primary growth factor underpinning this surge is the escalating demand for high-quality, specialized datasets to fuel advanced machine learning algorithms for optimizing renewable energy systems, forecasting, and asset management.

The growth of the Renewable Energy Machine Learning Dataset market is fundamentally propelled by the accelerating global transition toward clean energy sources. As nations strive to meet their decarbonization targets and integrate higher shares of renewables into their energy mix, the complexity of managing intermittent sources like solar and wind increases. This necessitates sophisticated machine learning models that require vast, accurate, and diverse datasets for training and validation. The proliferation of smart grids, IoT-enabled sensors, and remote monitoring technologies has resulted in an exponential increase in data generation, further fueling the demand for curated datasets tailored to the unique characteristics of renewable energy assets. In addition, government policies and international agreements encouraging renewable adoption are pushing utilities and energy companies to invest heavily in data infrastructure and analytics capabilities.

Another significant driver is the rising need for predictive analytics and real-time decision-making in renewable energy operations. Machine learning models trained on comprehensive datasets can deliver highly accurate forecasts of energy production, equipment failures, and market prices, enabling stakeholders to maximize efficiency and minimize downtime. This is particularly crucial for grid operators and energy traders who must balance supply and demand while mitigating the risks associated with renewables’ variability. The availability of diverse datasets—spanning historical weather patterns, sensor readings, energy output, and maintenance logs—empowers organizations to develop robust, adaptive algorithms that enhance the reliability and profitability of renewable assets. The push for digital transformation within the energy sector is further accelerating the adoption of machine learning datasets as a strategic asset.

The competitive landscape is also being shaped by the increasing collaboration between technology providers, research institutions, and energy companies. Open data initiatives and public-private partnerships are encouraging the development and sharing of standardized datasets, which in turn fosters innovation and lowers entry barriers for emerging players. At the same time, the rise of specialized dataset providers catering to niche segments—such as offshore wind or distributed solar—reflects the growing sophistication and segmentation of the market. These trends are expected to intensify as the industry matures, with data quality, accessibility, and interoperability emerging as key differentiators. The regional outlook for the Renewable Energy Machine Learning Dataset market is equally dynamic, with North America and Europe leading in adoption due to advanced grid infrastructure and supportive regulatory frameworks, while Asia Pacific is poised for the fastest growth driven by large-scale renewable deployments and digital transformation initiatives.

Dataset Type Analysis

The dataset type segment of the Renewable Energy Machine Learning Dataset market is characterized by a diverse range of data categories, each tailored to the unique requirements of different renewable energy sources. Solar datasets typically encompass irradiance measurements, panel performance data, weather conditions, and satellite imagery. The availability of granular solar datasets has accelerated the dev

The City and County Energy Profiles lookup table provides modeled electricity and natural gas consumption and expenditures, on-road vehicle fuel consumption, vehicle miles traveled, and associated emissions for each U.S. city and county. Please note this data is modeled and more precise data may be available from regional, state, or other sources. The modeling approach for electricity and natural gas is described in Sector-Specific Methodologies for Subnational Energy Modeling: https://www.nrel.gov/docs/fy19osti/72748.pdf. This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and complements the wealth of data, maps, and charts on the State and Local Planning for Energy (SLOPE) platform, available at the "Explore State and Local Energy Data on SLOPE" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.

This table contains figures on the supply and consumption of energy broken down by sector and by energy commodity. The energy supply is equal to the indigenous production of energy plus the receipts minus the deliveries of energy plus the stock changes. Consumption of energy is equal to the sum of own use, distribution losses, final energy consumption, non-energy use and the total net energy transformation. For each sector, the supply of energy is equal to the consumption of energy.

For some energy commodities, the total of the observed domestic deliveries is not exactly equal to the sum of the observed domestic receipts. For these energy commodities, a statistical difference arises that can not be attributed to a sector.

The breakdown into sectors follows mainly the classification as is customary in international energy statistics. This classification is based on functions of various sectors in the energy system and for several break downs on the international Standard Industrial Classification (SIC). There are two main sectors: the energy sector (companies with main activity indigenous production or transformation of energy) and energy consumers (other companies, vehicles and dwellings). In addition to a breakdown by sector, there is also a breakdown by energy commodity, such as coal, various petroleum products, natural gas, renewable energy, electricity and heat and other energy commodities like non renewable waste.

The definitions used in this table are exactly in line with the definitions in the Energy Balance table; supply, transformation and consumption. That table does not contain a breakdown by sector (excluding final energy consumption), but it does provide information about imports, exports and bunkering and also provides more detail about the energy commodities.

Data available: From: 1990.

Status of the figures: Figures up to and including 2022 are definite. Figures for 2023 and 2024 are revised provisional.

Changes as of July 2025: Compiling figures on solar electricity took more time than scheduled. Consequently, not all StatLine tables on energy contain the most recent 2024 data on production for solar electricity. This table contains the outdated data from June 2025. The most recent figures are 5 percent higher for 2024 solar electricity production. These figures are in these two tables (in Dutch): - StatLine - Zonnestroom; vermogen en vermogensklasse, bedrijven en woningen, regio - StatLine - Hernieuwbare energie; zonnestroom, windenergie, RES-regio Next update is scheduled in November 2025. From that moment all figures will be fully consistent again. We apologize for the inconvenience.

Changes as of June 2025: Figures for 2024 have been updated.

Changes as of March 17th 2025: For all reporting years the underlying code for 'Total crudes, fossil fraction' and 'Total kerosene, fossiel fraction' is adjusted. Figures have not been changed.

Changes as of November 15th 2024: The structure of the table has been adjusted. The adjustment concerns the division into sectors, with the aluminum industry now being distinguished separately within the non-ferrous metal sector. This table has also been revised for 2015 to 2021 as a result of new methods that have also been applied for 2022 and 2023. This concerns the following components: final energy consumption of LPG, distribution of final energy consumption of motor gasoline, sector classification of gas oil/diesel within the services and transfer of energy consumption of the nuclear industry from industry to the energy sector. The natural gas consumption of the wood and wood products industry has also been improved so that it is more comparable over time. This concerns changes of a maximum of a few PJ.

Changes as of June 7th 2024: Revised provisional figures of 2023 have been added.

Changes as of April 26th of 2024 The energy balance has been revised for 2015 and later on a limited number of points. The most important is the following: 1. For solid biomass and municipal waste, the most recent data have been included. Furthermore data were affected by integration with figures for a new, yet to be published StatLine table on the supply of solid biomass. As a result, there are some changes in receipts of energy, deliveries of energy and indigenous production of biomass of a maximum of a few PJ. 2. In the case of natural gas, an improvement has been made in the processing of data for stored LNG, which causes a shift between stock changes, receipts of energy and deliveries of energy of a maximum of a few PJ.

Changes as of March 25th of 2024: The energy balance has been revised and restructured. This concerns mainly the following: 1. Different way of dealing with biofuels that have been mixed with fossil fuels 2. A breakdown of the natural gas balance of agriculture into greenhouse horticulture and other agriculture. 3. Final consumption of electricity in services

1. Blended biofuels Previously, biofuels mixed with fossil fuels were counted as petroleum crude and products. In the new energy balance, blended biofuels count for renewable energy and petroleum crude and products and the underlying products (such as gasoline, diesel and kerosene) only count the fossil part of mixtures of fossil and biogenic fuels. To make this clear, the names of the energy commodities have been changed. The consequence of this adjustment is that part of the energy has been moved from petroleum to renewable. The energy balance remains the same for total energy commodities. The aim of this adjustment is to make the increasing role of blended biofuels in the Energy Balance visible and to better align with the Energy Balances published by Eurostat and the International Energy Agency. Within renewable energy, biomass, liquid biomass is now a separate energy commodity. This concerns both pure and blended biofuels.

2. Greenhouse horticulture separately The energy consumption of agriculture in the Netherlands largely takes place in greenhouse horticulture. There is therefore a lot of attention for this sector and the need for separate data on energy consumption in greenhouse horticulture. To meet this need, the agriculture sector has been divided into two subsectors: Greenhouse horticulture and other agriculture. For the time being, we only publish separate natural gas figures for greenhouse horticulture.

3. Higher final consumption of electricity in services in 2021 and 2022. The way in which electric road transport is treated has improved, resulting in an increase in the supply and final consumption of electricity in services by more than 2 PJ in 2021 and 2022. This also works through the supply of electricity in sector H (Transport and storage).

Changes as of November 14th 2023: Figures for 2021 and 2022 haven been adjusted. Figures for the Energy Balance for 2015 to 2020 have been revised regarding the following items: - For 2109 and 2020 final consumption of heat in agriculture is a few PJ lower and for services a few PJ higher. This is the result of improved interpretation of available data in supply of heat to agriculture. - During the production of geothermal heat by agriculture natural gas is produced as by-product. Now this is included in the energy balance. The amount increased from 0,2 PJ in 2015 to 0,7 PJ in 2020. - There are some improvements in the data for heat in industry with a magnitude of about 1 PJ or smaller. - There some other improvements, also about 1 PJ or smaller.

Changes as of June 15th 2023: Revised provisional figures of 2022 have been added.

Changes as of December 15th 2022: Figures for 1990 up to and including 2019 have been revised. The revision mainly concerns the consumption of gas- and diesel oil and energy commodities higher in the classification (total petroleum products, total crude and petroleum produtcs and total energy commodities). The revision is twofold: - New data for the consumption of diesel oil in mobile machine have been incorporated. Consequently, the final energy consumption of gas- and diesel oil in construction, services and agriculture increases. The biggest change is in construction (+10 PJ from 1990-2015, decreasing to 1 PJ in 2019. In agriculture the change is about 0.5-1.5 PJ from 2010 onwards and for services the change is between 0 and 3 PJ for the whole period. - The method for dealing with the statistical difference has been adapted. Earlier from 2013 onwards a difference of about 3 percent was assumed, matching old data (up to and including 2012) on final consumption of diesel for road transport based on the dedicated tax specifically for road that existed until 2012. In the new method the statistical difference is eliminated from 2015 onwards. Final consumption of road transport is calculated as the remainder of total supply to the market of diesel minus deliveries to users other than road transport. The first and second item affect both final consumption of road transport that decreases consequently about 5 percent from 2015 onwards. Before the adaption of the tax system for gas- and diesel oil in 2013 the statistical difference was positive (more supply than consumption). With the new data for mobile machines total consumption has been increased and the statistical difference has been reduced and is even negative for a few years.

Changes as of 1 March 2022: Figures for 1990 up to and including 2020 have been revised. The most important change is a different way of presenting own use of electricity of power-generating installations. Previously, this was regarded as electricity and CHP transformation input. From now on, this is seen as own use, as is customary in international energy statistics. As a result, the input and net energy transformation decrease and own use increases, on average about 15 PJ per year. Final consumers also have power generating installations. That's why final consumers now also have own use, previously this was not so. In the previous revision of 2021, the new sector blast

The Office of Manufacturing and Energy Supply Chains is responsible for strengthening and securing manufacturing and energy supply chains needed to modernize the nation’s energy infrastructure and support a clean and equitable energy transition. The office is catalyzing the development of an energy sector industrial base through targeted investments that establish and secure domestic clean energy supply chains and manufacturing, and by engaging with private-sector companies, other Federal agencies, and key stakeholders to collect, analyze, respond to, and share data about energy supply chains to inform future decision making and investment. The office manages programs that develop clean domestic manufacturing and workforce capabilities, with an emphasis on opportunities for small and medium enterprises and communities in energy transition. The Office of Manufacturing and Energy Supply Chains coordinates closely with the Office of Clean Energy Demonstrations for the management of major demonstration projects, and across all of DOE’s programs on manufacturing and supply chain issues, including with the Advanced Manufacturing Office in the Office of Energy Efficiency and Renewable Energy.

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.

The Integrated Energy Management and Forecasting Dataset is a comprehensive data collection specifically designed for advanced algorithmic modeling in energy management. It combines two distinct yet complementary datasets - the Energy Forecasting Data and the Energy Grid Status Data - each tailored for different but related purposes in the energy sector.

Success.ai’s Energy Consumption Data for European Energy Companies provides valuable insights into the operational landscapes of energy firms across Europe. Drawing from over 30 million verified company profiles, this dataset includes detailed information on energy consumption patterns, firmographic attributes, and decision-maker contacts within the European energy sector. Whether you are introducing smart grid technologies, offering renewable energy solutions, or analyzing regional consumption trends, Success.ai ensures that your strategic initiatives are informed by accurate, continuously updated, and AI-validated data.

Why Choose Success.ai’s European Energy Consumption Data?

1. Comprehensive Energy Company Insights

   • Access verified business locations, firmographic details, and key decision-maker profiles of utilities, independent power producers, grid operators, renewable energy firms, and energy consultancies.
   • AI-driven validation ensures 99% accuracy, allowing you to engage confidently with relevant stakeholders and reduce misdirected outreach.

2. Regional Focus on the European Market

   • Includes data on energy companies operating in the EU, EFTA countries, and neighboring markets, covering a wide range of regulatory environments and energy infrastructures.
   • Understand consumption patterns influenced by policy changes, seasonal demand fluctuations, and technological adoption rates unique to the European context.

3. Continuously Updated Datasets

   • Real-time updates reflect shifts in energy portfolios, leadership changes, market consolidations, and evolving consumption trends.
   • Keep pace with the dynamic European energy landscape, ensuring timely and relevant engagement opportunities.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing that your use of this data respects legal standards and industry best practices.

Data Highlights

• 30M+ Verified European Energy Companies Profiles: Includes energy firms across generation, transmission, distribution, and supply segments.
• Firmographic Details: Gain insights into company sizes, ownership structures, operational capacities, and geographic presence.
• Decision-Maker Contacts: Identify and connect with executives, energy managers, procurement officers, and regulatory liaisons influencing company strategies.
• Consumption Trends: Understand patterns related to energy sourcing, load management, efficiency initiatives, and sustainability goals.

Key Features of the Dataset:

1. Energy Sector Decision-Maker Profiles

   • Identify CEOs, CTOs, heads of procurement, and sustainability officers who shape purchasing decisions, investment priorities, and policy compliance.
   • Target professionals responsible for implementing new technologies, optimizing grids, and meeting regulatory benchmarks.

2. Advanced Filters for Precision Targeting

   • Filter companies by energy source (renewable, fossil, nuclear), size, region, or energy consumption levels.
   • Tailor campaigns to align with market maturity, environmental policies, grid integration projects, or decarbonization targets.

3. AI-Driven Enrichment

   • Profiles are enriched with actionable data, enabling you to customize messaging, highlight unique value propositions, and improve engagement outcomes with energy stakeholders.

Strategic Use Cases:

1. Sales and Partnership Development

   • Offer smart metering solutions, energy storage systems, or efficiency consulting services to grid operators, utilities, and industrial energy consumers.
   • Engage decision-makers who oversee supplier selection, technology adoption, and capital expenditure programs.

2. Market Research and Competitive Analysis

   • Analyze regional consumption patterns, emerging technologies, and demand-side management strategies to inform product development and pricing models.
   • Benchmark against leading firms to identify market gaps, growth opportunities, and evolving consumer preferences.

3. Regulatory Compliance and Sustainability Initiatives

   • Connect with energy companies focusing on sustainability, emission reductions, and compliance with EU energy directives.
   • Present solutions that help meet renewable energy mandates, improve energy storage capacity, or enhance grid resilience.

4. Investment and Project Financing

   • Identify energy firms and infrastructure projects ripe for investment, joint ventures, or green financing opportunities.
   • Reach out to executives managing portfolios, expansion plans, and risk management strategies in the European energy domain.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access high-quality, verified data at competitive prices, ensuring cost-effective strategies for market entry, partnership building, or product deployment.

2. Seamless Integration

   • Integrate verified ene...

Success.ai’s Oil & Gas Data with B2B CEO Contact Data for Global Energy Sector Executives offers businesses a powerful solution to connect with key decision-makers, influencers, and industry leaders across the energy spectrum. Drawing from over 170 million verified professional profiles, this dataset includes work emails, phone numbers, and enriched profiles of executives in oil and gas, renewable energy, utilities, and other energy-related sectors. Whether you’re targeting CEOs, operations managers, or sustainability directors, Success.ai ensures that you have the accurate and relevant information needed for effective outreach and strategic engagement.

Why Choose Success.ai’s Energy Sector Executive Data?

1. Comprehensive Contact Information
2. Access verified work emails, direct phone numbers, and LinkedIn profiles for executives and decision-makers in the global energy industry.

3. AI-driven validation ensures 99% accuracy, providing reliable data for sales, marketing, and partnership initiatives.

4. Global Reach Across Energy Verticals

5. Includes profiles of leaders in oil and gas, renewable energy, utilities, nuclear power, and emerging energy technologies.

6. Covers regions such as North America, Europe, Asia-Pacific, South America, and the Middle East, helping you connect with executives in established and emerging markets.

7. Continuously Updated Datasets

8. Real-time updates keep your data current, ensuring that your outreach remains timely, relevant, and competitive in a rapidly evolving industry.

9. Ethical and Compliant

10. Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring that all outreach and engagement strategies are ethically sourced and legally compliant.

Data Highlights

• 170M+ Verified Professional Profiles: Includes energy sector executives, managers, and thought leaders worldwide.
• 50M Work Emails: AI-validated for precise and effective communication.
• 30M Company Profiles: Deep insights into energy companies and their key personnel for informed targeting.
• 700M Global Professional Profiles: Enriched datasets to support comprehensive, scalable business strategies.

Key Features of the Dataset:

1. Energy Sector Decision-Maker Profiles
2. Identify and engage with C-suite executives, operations managers, sustainability directors, and other key influencers in the energy sector.

3. Connect with professionals who shape policy, direct investments, and lead initiatives in traditional and renewable energy fields.

4. Advanced Filters for Precision Targeting

5. Filter by industry segment (oil, gas, wind, solar, hydro, nuclear), company size, geographic location, and specific roles to focus your outreach on relevant contacts.

6. Refine campaigns to maximize engagement and conversion rates.

7. AI-Driven Enrichment

8. Profiles enriched with actionable data deliver valuable insights, ensuring that each interaction is timely, informed, and impactful.

Strategic Use Cases:

1. Sales and Business Development
2. Present technology solutions, equipment, or consulting services directly to decision-makers in the energy sector.

3. Forge relationships with executives responsible for procurement, strategic partnerships, and operational efficiency.

4. Marketing and Brand Awareness

5. Launch targeted campaigns to promote energy-related software, sustainable energy solutions, or investment opportunities.

6. Leverage accurate contact data to increase engagement and drive better campaign results.

7. Investment and M&A Activities

8. Connect with key players in energy startups, established utilities, and global energy conglomerates exploring mergers, acquisitions, or investment deals.

9. Identify the right decision-makers to streamline negotiations and capital deployment.

10. Sustainable and Renewable Energy Initiatives

11. Engage leaders in the renewable energy space to foster partnerships, promote clean energy solutions, and encourage sustainable practices.

12. Position your business as a strategic ally in achieving long-term environmental and economic goals.

Why Choose Success.ai?

1. Best Price Guarantee

2. Access premium-quality verified data at competitive prices, ensuring maximum return on investment.

3. Seamless Integration

4. Incorporate the data into your CRM or marketing automation tools using APIs or custom download formats.

5. Data Accuracy with AI Validation

6. Trust in 99% data accuracy for confident decision-making, strategic targeting, and consistent outreach results.

7. Customizable and Scalable Solutions

8. Tailor datasets to meet your unique objectives, whether focusing on a specific region, energy vertical, or company size.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enrich your existing records with verified contact data for energy sector executives, improving targeting and personalization.

3. Lead Generation API

4. Automate lead...

The SmartPowerML dataset is a user-level dataset designed to simulate the operation of a Power Marketing Management Platform (PMMP) enhanced by machine learning. It captures detailed information on users’ electricity consumption behaviors, demographics, pricing plans, and marketing engagement. The dataset supports use cases such as consumption forecasting, dynamic pricing strategy development, user segmentation, and targeted marketing in the energy sector.

This dataset is ideal for researchers and developers looking to build or evaluate machine learning models in the fields of energy analytics, smart grid management, and personalized marketing.

Key Features User Demographics: Includes age, location, and household size.

Electricity Consumption: Daily, monthly, peak, and time-of-day usage patterns.

Marketing Engagement: Tracks user interaction with marketing campaigns.

Pricing Plans: Indicates user enrollment in dynamic or flat rate plans.

Incentive Participation: Flags users who opted into energy-saving incentive programs.

Behavioral Metrics: Contains user engagement rates and energy usage reduction percentages.

Target Variable: Energy Usage Reduction (%) to support regression modeling and impact analysis.

Eneroutlook provides global energy and climate scenarios at global or region level Global Energy Outlook (EnerOutlook) is a free online interactive data software, allowing to browse data through intuitive maps and graphs, for a visual analysis of the expected long-term trends in the energy industry. These can be viewed globally and by world region.

The data has been sourced from the International Renewable Energy Agency (https://pxweb.irena.org/pxweb/en/IRENASTAT). The indicators on energy transition have been formulated to help users understand the progress in the adoption of renewable energy sources vis-à-vis the increasing energy requirements.Sources: International Renewable Energy Agency (IRENA) (2022), Renewable Energy Statistics 2022, https://pxweb.irena.org/pxweb/en/IRENASTAT; IMF Staff Calculations.Category: Mitigation,Transition to a Low-Carbon Economy Data series: Electricity GenerationElectricity Installed Capacity Metadata:Electricity generation: The gross electricity produced by electricity plants, combined heat and power plants (CHP) and the distribution generators measured at the output terminals of generation. It includes on-grid and off-grid generation, and it also includes the electricity self-consumed in energy industries; not only the electricity fed into the grid (net electricity generation). The indicator is expressed in the Dashboard in Gigawatt hours (GWh).Electricity Installed Capacity: The maximum active power that can be supplied continuously (i.e., throughout a prolonged period in a day with the whole plant running) at the point of outlet (i.e. after taking the power supplies for the station auxiliaries and allowing for the losses in those transformers considered integral to the station). This assumes no restriction of interconnection to the network. It does not include overload capacity that can only be sustained for a short period of time (e.g., internal combustion engines momentarily running above their rated capacity). For most countries and technologies, the data on installed capacity on the Dashboard reflects the capacity installed and connected at the end of the calendar year and are expressed in Mega Watts (MW). The renewable power capacity data shown in these tables represents the maximum net generating capacity of power plants and other installations that use renewable energy sources to produce electricity. For most countries and technologies, the data reflects the capacity installed and connected at the end of the calendar year. Pumped storage is included in total capacity but excluded from total generation. The capacity data are presented in megawatts (MW) and the generation data are presented in gigawatt-hours (GWh). All the data are rounded to the nearest one MW/GWh, with figures between zero and 0.5 shown as a 0.

This dataset is about companies. It has 13,246 rows and is filtered where the sector is Energy. It features 30 columns including city, country, employees, and employee type.

The Industrial Energy Data Book (IEDB) aggregates and synthesizes information on the trends in industrial energy use, energy prices, economic activity, and water use. The IEDB also estimates county-level industrial energy use and combustion energy use of large energy-using facilities (i.e., facilities required to report greenhouse gas emissions under the EPA's Greenhouse Gas Reporting Program). These estimates are derived from publicly available sources from EPA, Energy Information Administration, Census Bureau, USDA, and USGS. The estimation methodology is meant to be improved over time with input from the energy analysis and developer communities. Please refer to https://github.com/NREL/Industry-energy-data-book.

The UK's direct use of energy from fossil fuels and other sources (nuclear, net imports, renewables, biofuels and waste and reallocated use of energy by industry (SIC 2007 section - 21 categories), 1990 to 2023.

Global consumption of renewable energy has increased significantly over the last two decades. Consumption levels nearly reached ***** exajoules in 2024. This upward trend reflects the increasing adoption of clean energy technologies worldwide. However, despite its rapid growth, renewable energy consumption still remains far below that of fossil fuels. Fossil fuels still dominate energy landscape While renewable energy use has expanded, fossil fuels continue to dominate the global energy mix. Coal consumption reached *** exajoules in 2023, marking its highest level to date. Oil consumption also hit a record high in 2024, exceeding *** billion metric tons for the first time. Natural gas consumption has remained relatively stable in recent years, hovering around **** trillion cubic meters annually. These figures underscore the ongoing challenges in transitioning to a low-carbon energy system. Renewable energy investments The clean energy sector has experienced consistent growth over the past decade, with investments more than doubling from *** billion U.S. dollars in 2014 to *** billion U.S. dollars in 2023. China has emerged as the frontrunner in renewable energy investment, contributing *** billion U.S. dollars in 2023. This substantial funding has helped propel the renewable energy industry forward.

Abstract copyright UK Data Service and data collection copyright owner. The International Energy Agency (IEA) datasets published by the Energy Statistics Division (ESD) contain annual and quarterly time series data from 1960 onwards on energy production, trade, stocks, transformation, consumption, prices and taxes as well as on greenhouse gas emissions for OECD Member countries and non-OECD countries world-wide. In OECD Member countries the data are collected by official bodies (most often the national statistics office in each country) from firms, government agencies and industry organisations and are then reported to the IEA using questionnaires to ensure international comparability. In non-OECD countries the data are collected directly from government and industry contacts and from national publications. The International Energy Agency (IEA) Coal Information database contains a complete time series of coal statistics corresponding to the data shown in Part II of the annual IEA publication Coal Information. Selected tables from Part I of the publication are also included, as well as a database of worldwide coal statistics covering production, trade, use in transformation (electricity and heat production) and final consumption in industry and other sectors. The Coal Information database contains a time series of annual coal data for Organisation for Economic Co-operation and Development (OECD) and non-OECD countries from 1960 onwards. The database is updated by the IEA in July each year. These data were first provided by the UK Data Service in June 2005 and are updated annually.

Facility-level industrial combustion energy use is calculated from greenhouse gas emissions data reported by large emitters (>25,000 metric tons CO2e per year) under the U.S. EPA's Greenhouse Gas Reporting Program (GHGRP, https://www.epa.gov/ghgreporting). The calculation applies EPA default emissions factors to reported fuel use by fuel type. Additional facility information is included with calculated combustion energy values, such as industry type (six-digit NAICS code), location (lat, long, zip code, county, and state), combustion unit type, and combustion unit name. Further identification of combustion energy use is provided by calculating energy end use (e.g., conventional boiler use, co-generation/CHP use, process heating, other facility support) by manufacturing NAICS code. Manufacturing facilities are matched by their NAICS code and reported fuel type with the proportion of combustion fuel energy for each end use category identified in the 2010 Energy Information Administration Manufacturing Energy Consumption Survey (MECS, http://www.eia.gov/consumption/manufacturing/data/2010/). MECS data are adjusted to account for data that were withheld or whose end use was unspecified following the procedure described in Fox, Don B., Daniel Sutter, and Jefferson W. Tester. 2011. The Thermal Spectrum of Low-Temperature Energy Use in the United States, NY: Cornell Energy Institute.

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The UK's energy use by industry (SIC 2007 group - around 130 categories), source (for example, industrial and domestic combustion, aircraft, road transport and so on - around 80 categories) and fuel (for example, anthracite, peat, natural gas and so on - around 20 categories), 1990 to 2023.