The Report On Big Data Analytics Market in the Energy Sector is Segmented by Application (Grip Operations, Smart Metering, Asset, And Workforce Management) and Geography (North America, Europe, Asia-pacific, Latin America, And Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Success.ai’s LinkedIn Company Data for the Renewable Energy Sector Worldwide provides a powerful and accurate dataset tailored for businesses and organizations aiming to connect with renewable energy companies and professionals globally. Covering roles and firms involved in solar, wind, hydro, and other renewable energy solutions, this dataset offers verified LinkedIn profiles, firmographic insights, and detailed decision-maker data.

With access to over 700 million verified global profiles, Success.ai ensures your marketing, outreach, and strategic initiatives are driven by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to succeed in the fast-evolving renewable energy industry.

Why Choose Success.ai’s LinkedIn Company Data?

1. Verified Profiles for Precision Engagement

   • Access verified LinkedIn profiles, employee counts, and decision-maker data for renewable energy companies worldwide.
   • AI-driven validation ensures 99% accuracy, reducing inefficiencies and boosting outreach effectiveness.

2. Comprehensive Global Coverage

   • Includes renewable energy businesses from North America, Europe, Asia-Pacific, and other key markets.
   • Gain insights into regional energy trends, innovative technologies, and company expansions in the renewable sector.

3. Continuously Updated Datasets

   • Real-time updates capture changes in organizational structures, employee roles, and firm locations.
   • Stay aligned with market shifts to capitalize on emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Access LinkedIn company data for renewable energy professionals and organizations worldwide.
• Firmographic Insights: Gain employee counts, business locations, and operational details to refine targeting.
• Decision-maker Profiles: Connect with executives, project managers, and engineers shaping renewable energy innovations.
• Industry Trends: Leverage actionable data to understand growth areas, company expansions, and adoption of renewable solutions.

Key Features of the Dataset:

1. Comprehensive Company Profiles in Renewable Energy

   • Identify and connect with renewable energy companies specializing in solar, wind, geothermal, and other sustainable solutions.
   • Target professionals driving green initiatives, energy storage technologies, and project development.

2. Advanced Filters for Precision Campaigns

   • Filter companies and professionals by industry focus, geographic location, or operational size.
   • Tailor campaigns to align with specific needs, such as promoting energy technologies or forming strategic partnerships.

3. Regional and Industry-specific Insights

   • Leverage data on renewable energy adoption, project pipelines, and investment trends across key global regions.
   • Refine strategies to align with market priorities and regional demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Marketing Campaigns and Outreach

   • Promote renewable energy technologies, services, or consulting solutions to businesses and decision-makers in the sector.
   • Use verified contact data for multi-channel outreach, including email, phone, and LinkedIn.

2. Partnership Development and Collaboration

   • Build relationships with renewable energy companies and stakeholders exploring project funding, technology partnerships, or market entry.
   • Foster collaborations that advance green energy adoption and operational efficiencies.

3. Market Research and Competitive Analysis

   • Analyze trends in the renewable energy market to refine product offerings, marketing strategies, and investment plans.
   • Benchmark against competitors to identify growth opportunities and emerging market needs.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers recruiting for roles in renewable energy project management, engineering, and sustainability leadership.
   • Provide workforce optimization platforms or talent development tools tailored to the renewable energy industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality LinkedIn company data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration

   • Integrate verified LinkedIn company data into CRM systems, analytics tools, or marketing platforms via APIs or downloadable formats, enhancing productivity and simplifying workflows.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven decisions...

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The artificial intelligence in energy market share is expected to increase by USD 6.78 billion from 2020 to 2025, and the market’s growth momentum will decelerate at a CAGR of 34.19%.

This artificial intelligence in energy market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers artificial intelligence in energy market segmentations by solution (software, hardware, and services) and geography (North America, Europe, APAC, MEA, and South America). The artificial intelligence in energy market report also offers information on several market vendors, including ABB Ltd., Alphabet Inc., Flex Ltd., General Electric Co., Intel Corp., International Business Machines Corp., Microsoft Corp., Origami Energy Ltd., Siemens AG, and Verdigris Technologies Inc. among others.

What will the Artificial Intelligence In Energy Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Artificial Intelligence in Energy Market Size for the Forecast Period and Other Important Statistics

Artificial Intelligence In Energy Market: Key Drivers, Trends, and Challenges

Based on our research output, there has been a positive impact on the market growth during and post COVID-19 era. The growing demand for data integration and visual analytics is notably driving the artificial intelligence in energy market growth, although factors such as existing issues of ai may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the artificial intelligence in energy market industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Artificial Intelligence In Energy Market Driver

One of the key factors driving the global AI market is the growing demand for data integration and visual analytics. Rising proliferation and complexity have made the process of deploying and maintaining reliable data interfaces difficult. Enterprises around the world are, therefore, adopting data integration solutions. AI allows real-time synthesizing of data to facilitate real-time analysis for effective decision-making, thus enabling enterprises to monitor, transform, and deliver data; understand business processes; and bridge the gap between businesses and IT. Similarly, AI helps energy companies to integrate technical and business process data from different sources and convert them into meaningful business insights. With the exponential increase in data volume, the need for analyzing, transforming, monitoring, and interpreting data has become a priority for business operations. With globalization, customers, suppliers, and companies are scattered across the world and require real-time information exchange. To accomplish this, energy companies require AI platforms to link multiple enterprise systems with the web and cloud-based applications. Additionally, energy companies are integrating data with AI-powered video analytics systems to explore and analyze various types of data, such as sales data, for informed decision-making. Enterprises are also integrating business analytics software with their businesses for the dynamic representation of data. Hence, the demand for AI in the energy sector is likely to increase significantly during the forecast period.

Key Artificial Intelligence In Energy Market Trend

Increasing adoption of cloud-based solutions is another factor supporting the global AI market growth in the forecast period. With the increasing applications of robotics in repetitive and risky tasks, end-users are increasingly seeking avenues to ensure the elimination of limitations of industrial automation and robotics technologies. These limitations arise due to factors such as the cost, computational capacity, storage, size, power supply, motion mode, and working environment. Thus, the adoption of cloud-based AI solutions is increasing in the energy sector to enhance the capabilities of existing systems. Furthermore, the emergence of AI-as-a-service (AIaaS) is trending among various industrial users of AI, as it allows individuals and companies to access AI for various applications without large initial investment and with a lower risk of failure. AIaaS can allow energy companies to experiment on samples of multiple public cloud platforms to test various machine learning algorithms. AIaaS helps vendors in the market to increase their awareness about AI and its benefits, such as efficiency and maintenance of a company’s grid system and asset management of solar farms and gas plants. Companies like Alphabet, IBM, and GENERAL ELECTRIC are investing heavily in the development of prediction and maintenance systems for the energy industry and are planning

We present a real-world data set obtained from monitoring a smart company building over the course of six years. The data set describes the energy consumption of various sites within the building, energy production via a photovoltaic system and a combined-heat-and-power plant, and the detailed operation of the heating and cooling system. The data set further contains measurements from an on-site weather station for the same time period. The data set covers periods of normal operation before the onset of the Covid-19-pandemic, periods of reduced operation during, and after, the pandemic. We describe the recording, processing, and curation strategy to generate the data set. The data set enables the application of a wide range of methods in the domain of energy management, including optimization, modelling, and machine learning to optimize building operations and reduce costs and carbon emissions., During the recording time span, a multitude of issues occurred which affected the collected data, like measurement outages, maintenance and device replacements. In order to produce a consistent and research-grade data set, these issues need to be addressed and corrected. We apply a cleaning and post-processing pipeline to the data, which consists of seven steps:

Specification and detection of issues with rule-based detection mechanism Data harmonization to ensure consistency in naming and sign convection Application of issue correction Time alignment of all measurements Resampling into equidistantly sampled time series (1 min, 15 min, 1 h) Calculation of missing dependent measurements Export the time series in gzip-compressed CSV files

Furthermore, based on the corrected and resampled time series, we provide a reduced dataset. It consists of a less complex representation of the building energy consumption, production of both electricity, heating and cooling, as well as weather measure..., , # A Real-World Energy Management data set from a Smart Company Building for Optimization and Machine Learning

https://doi.org/10.5061/dryad.73n5tb363

Description of the data and file structure

The presented data set contains measurements from electricity meters, heat and cooling meters and the weather station from a medium size company, the Honda R&D Europe facility located in Offenbach am Main, Germany. The data set contains measurements from January 1, 2018 0:00 GMT+1 until January 1, 2024 0:00 GMT+1. Note that the facility is located in Offenbach, Germany, hence the local timezone is Europe/Berlin, which corresponds to GMT+2 during the European daylight savings period, and GMT+1 in winter.

As electricity meters, ABB-B24, Janitza UMG 96 RM-E, Janitza UMG 96 PA MID+, as well as Socomec DIRIS I35, I45 and S135 meters are installed in the facility. Heating and cooling is metered using SensorStar 2/2U meters. Weather measurements are c...

Google’s energy consumption has increased over the last few years, reaching 25.9 terawatt hours in 2023, up from 12.8 terawatt hours in 2019. The company has made efforts to make its data centers more efficient through customized high-performance servers, using smart temperature and lighting, advanced cooling techniques, and machine learning. Datacenters and energy Through its operations, Google pursues a more sustainable impact on the environment by creating efficient data centers that use less energy than the average, transitioning towards renewable energy, creating sustainable workplaces, and providing its users with the technological means towards a cleaner future for the future generations. Through its efficient data centers, Google has also managed to divert waste from its operations away from landfills. Reducing Google’s carbon footprint Google’s clean energy efforts is also related to their efforts to reduce their carbon footprint. Since their commitment to using 100 percent renewable energy, the company has met their targets largely through solar and wind energy power purchase agreements and buying renewable power from utilities. Google is one of the largest corporate purchasers of renewable energy in the world.

Find verified company profiles for the energy industry in Europe with Success.ai, including access to Energy Consumption Data. Includes business locations, firmographic details, and decision-maker insights. Continuously updated datasets. Best price guaranteed.

Provide relevant information about the names, models, capacities, addresses, and other details of renewable energy sites operated by Taiwan Power Company.

The Green Data Center Market size was valued at USD 70.14 USD Billion in 2023 and is projected to reach USD 206.76 USD Billion by 2032, exhibiting a CAGR of 16.7 % during the forecast period. A green data center is a service facility which utilizes energy-efficient technologies. They do not contain obsolete systems and take advantage of newer, more efficient technologies. With the exponential growth and usage of the Internet, power consumption in data centers has increased significantly. Due to the resulting environmental impact, increase in public awareness, higher cost of energy and legislative action, increased pressure has been placed on companies to follow a green policy. For these reasons, the creation of sustainable data centers has become essential in an environmental and a business sense. With substantial energy demands for operation and the need to develop efficient cooling technology to safeguard equipment, data centers present a particularly interesting field for achieving sustainability. And, at the center of this need, lies energy efficiency. Recent developments include: December 2023: Liberation Technology Services (LTS) partnered with E-New Data Corp. to revolutionize the data centre landscape by building a state-of-the-art, environmentally friendly facility in 2024. The partnership between LTS and E-New Data Co., Ltd. helps significantly reduce power and water consumption compared to traditional data centres., May 2023: Sonic Edge, a U.K.-based modular data centre firm, partnered with Deep Green to offer its customers low power costs by using immersion cooling technologies. The partnership will aid Sonic Edge in launching 50 new HPC/EdgePods across the U.K. by 2024., February 2023: Micro Hub opened a solar-powered data center in Dubai by partnering with technology players such as Dell Technologies, Microsoft, Huawei, and VMWare. The data centre utilizes 100% renewable energy and includes the latest advances such as Internet of Things (IoT), digital twin technologies, cybersecurity, Artificial Intelligence (AI), and others., October 2022: PhonePe, with the help of Dell Technologies and NTT, launched its first data center in India. The data center was designed and built with advanced green cooling technologies, such as Liquid Immersion Cooling (LIC) and Contact Liquid Cooling (DCLC)., September 2022: NEC Corporation planned to establish two data centers utilizing 100% renewable energy. The NEC Kanagawa Data Center and the NEC Kobe Data Center would be built and designed specifically to reduce greenhouse gas emissions.. Key drivers for this market are: Rapid Emergence of Artificial Intelligence in Power & Cooling Technologies to Drive Market Growth. Potential restraints include: Cost Considerations Associated with Initial Investment & Modifying Existing Infrastructure to Impede Market Progress. Notable trends are: Increasing Adoption of Renewable Energy Resources to Reduce Carbon Footprint will Bolster Market Growth.

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.

Global Energy and Utilities Analytics market size is expected to reach $7.16 billion by 2029 at 16.9%, smart meter deployment drives growth in the energy and utilities analytics market

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...

Peru Energy Sales: Turnover: Consumer: Edelnor data was reported at 68.000 USD mn in Dec 2016. This records an increase from the previous number of 64.300 USD mn for Nov 2016. Peru Energy Sales: Turnover: Consumer: Edelnor data is updated monthly, averaging 41.650 USD mn from Jan 2002 (Median) to Dec 2016, with 180 observations. The data reached an all-time high of 79.300 USD mn in Dec 2014 and a record low of 22.300 USD mn in Aug 2002. Peru Energy Sales: Turnover: Consumer: Edelnor data remains active status in CEIC and is reported by Oversight Organism for Investment on Energy & Mining. The data is categorized under Global Database’s Peru – Table PE.RB005: Energy Sales: Turnover: by Company.

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.

The World Bank working with EED Advisory Limited carried out the Burundi MTF energy survey to provide national-level insights into the status of access to electricity. The survey extended to identify 120 high-potential mini-grids sites in the country. These shapefiles demonstrate; i) 120 high-potential mini-grids sites with basic information including the number of health and education facilities in the community ii) Detailed household data of 6 villages among 120 potential mini-grids sites, including types of houses, the existence of lightning, solar, radio, tv, mobile, etc. iii) Detailed business data of 6villages among 120 potential mini-grids sites, including hours of operation for the business, the existence of lightning, solar, radio, tv, mobile, etc. iv) Potential customers data of 6 villages among 120 potential mini-grids sites, including the types of customers (household or business). The format includes a zip file (which includes datasets of shapefile format and excel spreadsheets).

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2022, figures for 2023 are revised provisional figures and figures for 2024 are provisional.

Changes as of june 2025: Figures for 2024 have been added.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

Energy and utilities data from the Alaska Energy Authority, Alaska Energy Data Gateway. Includes: - Hydroelectric - Hydrokinetic - Wind Power - Thermal Areas - Hot Springs - Sawmills - Energy Regions - Electric Utility Lines - TAPS Pipeline - Volanoes and Vents - Solar PowerSource: Alaska Energy AuthorityThis data is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Energy Data Gateway

The global Electric Energy Data Collection Terminal market is experiencing robust growth, driven by the increasing demand for smart grids, renewable energy integration, and advanced energy management systems. The market is projected to reach a value of $5 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 10% during the forecast period (2025-2033). This significant expansion is fueled by several key factors. Firstly, the global push towards decarbonization and the consequent surge in renewable energy sources necessitates efficient monitoring and management of energy flows. Electric Energy Data Collection Terminals play a crucial role in this process, providing real-time data on energy consumption, production, and grid stability. Secondly, the increasing adoption of smart city initiatives is creating a substantial demand for advanced metering infrastructure (AMI) and smart grid technologies, which rely heavily on these terminals for data acquisition and analysis. Furthermore, the growing need for improved grid reliability and reduced energy losses is also driving market expansion. The market is segmented based on technology, application, and geography, with significant regional variations in growth rates due to factors such as government policies, infrastructure development, and technological adoption rates. Key players in the market, including Mitsubishi Electric, WAGO Group, and Yokogawa Electric Corporation, are focusing on developing advanced solutions with enhanced functionalities, such as improved data analytics capabilities and seamless integration with existing energy management systems. The competitive landscape is characterized by both established players and emerging companies, leading to increased innovation and market diversification. However, factors such as high initial investment costs and the complexity of integrating these terminals into existing infrastructure might pose some challenges to market growth in the near term. Nevertheless, the long-term outlook for the Electric Energy Data Collection Terminal market remains extremely positive, given the sustained global emphasis on sustainable energy practices and the ongoing digital transformation of the energy sector.

This contains all reported and validated M&O Prime Contractor 1st tier subcontractor small business award dollars for the government’s most recently completed fiscal year. The file contains 55 data elements that represent a subset of FPDS-NG data.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

EC: EP: MFG: EE: Data Storage Media Units & Reproducing data was reported at 4,897,320.596 kWh th in 2017. This records an increase from the previous number of 4,807,091.195 kWh th for 2016. EC: EP: MFG: EE: Data Storage Media Units & Reproducing data is updated yearly, averaging 2,968,054.869 kWh th from Dec 1998 (Median) to 2017, with 20 observations. The data reached an all-time high of 4,897,320.596 kWh th in 2017 and a record low of 249,537.430 kWh th in 1998. EC: EP: MFG: EE: Data Storage Media Units & Reproducing data remains active status in CEIC and is reported by Taiwan Power Company. The data is categorized under Global Database’s Taiwan – Table TW.RB007: Energy Consumption: Electricity: By Industry: Taiwan Power Company (Annual).