The Global Renewable Energy and Indicators Dataset is a comprehensive resource designed for in-depth analysis and research in the field of renewable energy. This dataset includes detailed information on renewable energy production, socio-economic factors, and environmental indicators from around the world. Key features include:

1.Renewable Energy Data: Covers various types of renewable energy sources such as solar, wind, hydro, and geothermal energy, detailing their production (in GWh), installed capacity (in MW), and investments (in USD) across different countries and years.

2.Socio-Economic Indicators: Includes data on population, GDP, energy consumption, energy exports and imports, CO2 emissions, renewable energy jobs, government policies, R&D expenditure, and renewable energy targets.

3.Environmental Factors: Provides information on average annual temperature, annual rainfall, solar irradiance, wind speed, hydro potential, geothermal potential, and biomass availability.

4.Additional Features: Contains relevant features such as energy storage capacity, grid integration capability, electricity prices, energy subsidies, international aid for renewables, public awareness scores, energy efficiency programs, urbanization rate, industrialization rate, energy market liberalization, renewable energy patents, educational level, technology transfer agreements, renewable energy education programs, local manufacturing capacity, import tariffs, export incentives, natural disasters, political stability, corruption perception index, regulatory quality, rule of law, control of corruption, economic freedom index, ease of doing business, innovation index, number of research institutions, renewable energy conferences, renewable energy publications, energy sector workforce, proportion of energy from renewables, public-private partnerships, and regional renewable energy cooperation.

This dataset is ideal for analysts, researchers, and policymakers aiming to study trends, impacts, and strategies related to renewable energy development globally.

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[ Total 17 file , 74 columns, World Wide, 57 Years data ]

The Renewable Energy World Wide dataset is a comprehensive collection of global renewable energy data from 1965 to 2022. The dataset includes information on hydropower, wind, solar, biofuel, and geothermal energy production from around the world.

Since the Industrial Revolution, the energy mix of most countries across the world has become dominated by fossil fuels. This has major implications for the global climate, as well as for human health. To reduce CO2 emissions and local air pollution, the world needs to rapidly shift towards low-carbon sources of energy – nuclear and renewable technologies. Renewable energy will play a key role in the decarbonization of our energy systems in the coming decades. But how rapidly is our production of renewable energy changing? What technologies look most promising in transforming our energy mix?

Data vastness of this dataset:

1. renewable-share-energy data.
2. modern-renewable-energy-consumption.
3. modern-renewable-prod
4. share-electricity-renewables
5. hydropower-consumption
6. hydro-share-energy
7. share-electricity-hydro
8. wind-generation
9. cumulative-installed-wind-energy-capacity-gigawatts
10. wind-share-energy
11. share-electricity-wind
12. solar-energy-consumption
13. installed-solar-PV-capacity
14. solar-share-energy
15. share-electricity-solar
16. biofuel-production
17. installed-geothermal-capacity

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.

U.S. Monthly Renewable Energy Consumption by Source and Sector (1973-2024)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2F0fe60a09cda8f60e446422f6721e68f5%2Frenewable%20energy%20consumption%20flag.png?generation=1715139420693463&alt=media" alt=""> This dataset provides monthly data on renewable energy consumption in the United States from January 1973 to December 2024, broken down by energy source and consumption sector. The data is sourced from the U.S. Energy Information Administration (EIA).

Renewable energy has become an increasingly important part of the U.S. energy mix in recent years as the country seeks to reduce its greenhouse gas emissions and dependence on fossil fuels. This dataset allows for detailed analysis of renewable energy trends over time and across different sectors of the economy.

IMPORTANT: Dataset Info

• Every entry that has a value of 0 means that the datapoint was either "Not Available," "No Data Reported," or "Not Meaningful"
• You most likely want to exclude the column titled Total Renewable Energy from your comparative analysis across fuel types as it represents the sum of the others

Columns

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.

The Foundational Industry Energy Dataset (FIED) addresses several of the areas of growing disconnect between the demands of industrial energy analysis and the state of industrial energy data by providing unit-level characterization by facility. Each facility is identified by a unique registryID, based on the U.S. Environmental Protection Agency (EPA) Facility Registry Service, and includes its coordinates and other geographic identifiers. Energy-using units are characterized by design capacity, as well as their estimated energy use, greenhouse gas emissions, and physical throughput using 2017 data from the EPA's National Emissions Inventory and Greenhouse Gas Reporting Program. An overview of the derivation methods is provided in a separate technical report which will be linked after publication. The Python code used to compile the dataset is available in a GitHub repository. An updated 2020 version is under development.

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 UK's energy use from renewable and waste sources, by source (for example, hydroelectric power, wind, wave, solar, and so on) and industry (SIC 2007 section - 21 categories), 1990 to 2023.

There were approximately **** million jobs in the renewable energy industry worldwide in 2023. Renewable job numbers have been steadily rising over the past decade, increasing from *** million in 2012. The technology with the most number of jobs in 2023 was solar photovoltaics.

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...

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...

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.

Renewable Energy Market Making Algorithm Market Outlook

According to our latest research, the global Renewable Energy Market Making Algorithm market size reached USD 2.18 billion in 2024, reflecting robust momentum in the adoption of algorithm-driven trading and optimization within renewable energy markets. The market is experiencing a strong compound annual growth rate (CAGR) of 18.7%, positioning the sector for a substantial expansion to USD 10.68 billion by 2033. This growth is primarily attributed to the increasing integration of renewable energy sources into power grids, the need for efficient trading mechanisms, and the rapid digitalization of energy markets worldwide.

The acceleration in the adoption of renewable energy market making algorithms is largely driven by the global shift towards sustainable energy sources and the growing complexity of energy trading environments. As governments and regulatory bodies introduce ambitious decarbonization targets, the volume and volatility of renewable energy entering the grid are rising. This dynamic environment necessitates sophisticated algorithmic solutions capable of managing real-time market operations, optimizing price discovery, and ensuring liquidity. Additionally, the proliferation of distributed energy resources and the increasing participation of independent power producers have created a highly competitive landscape, spurring the demand for advanced algorithms that can provide a competitive edge in electricity and carbon credit trading.

Technological advancements in artificial intelligence (AI), machine learning, and cloud computing are further propelling the growth of the Renewable Energy Market Making Algorithm market. These technologies enable the development of highly adaptive and predictive algorithms that can analyze vast datasets, forecast market trends, and execute trades with minimal latency. The integration of AI-driven analytics into market making algorithms allows for more accurate risk assessment, improved grid balancing, and enhanced decision-making capabilities. As a result, energy market participants are increasingly investing in software and hardware solutions that leverage these innovations to maximize trading efficiency and profitability.

Another significant growth factor is the emergence of new market structures and trading mechanisms tailored to renewable energy assets. The introduction of renewable energy certificates, carbon credit trading platforms, and peer-to-peer energy trading models has created new opportunities for algorithmic market making. These developments are supported by regulatory frameworks that encourage transparency, fairness, and liquidity in renewable energy markets. Moreover, the growing adoption of cloud-based deployment models is making advanced market making algorithms more accessible to a broader range of market participants, from large utilities to small independent power producers and energy traders.

The role of data in renewable energy markets cannot be overstated, particularly with the emergence of the Renewable Energy Machine Learning Dataset. This dataset is instrumental in training algorithms to predict energy production and consumption patterns, thus enhancing the accuracy of market forecasts. By leveraging vast amounts of historical and real-time data, machine learning models can identify trends and anomalies that would be challenging for traditional methods to detect. This capability is crucial in optimizing trading strategies and ensuring efficient market operations. As the renewable energy sector continues to grow, the demand for comprehensive datasets that support machine learning applications is expected to rise, driving further innovation and efficiency in market making algorithms.

From a regional perspective, North America and Europe are leading the adoption of renewable energy market making algorithms, owing to their mature energy markets, supportive regulatory environments, and significant investments in grid modernization. The Asia Pacific region is also witnessing rapid growth, driven by the expansion of renewable energy capacity in countries such as China, India, and Japan. Latin America and the Middle East & Africa are gradually catching up, supported by increasing renewable energy investments and th

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.

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...

Kenya KE: Renewable Energy Consumption: % of Total Final Energy Consumption data was reported at 72.663 % in 2015. This records a decrease from the previous number of 75.518 % for 2014. Kenya KE: Renewable Energy Consumption: % of Total Final Energy Consumption data is updated yearly, averaging 79.485 % from Dec 1990 (Median) to 2015, with 26 observations. The data reached an all-time high of 83.183 % in 2003 and a record low of 72.663 % in 2015. Kenya KE: Renewable Energy Consumption: % of Total Final Energy Consumption data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Energy Production and Consumption. Renewable energy consumption is the share of renewables energy in total final energy consumption.; ; World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.; Weighted average;

The indicator measures the share of renewable energy consumption in gross final energy consumption according to the Renewable Energy Directive. The gross final energy consumption is the energy used by end-consumers (final energy consumption) plus grid losses and self-consumption of power plants.

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 dataset comprises the following series:

01_RI_data_series: Return index series for the 27 companies included in the NASDAQ OMX Renewable Energy Gen (GRNREG) index (source: Datastream). 02_DY_data_series: Dividend yield series for the 27 companies included in the NASDAQ OMX Renewable Energy Gen (GRNREG) index (source: Datastream). 03_MV_data_series: Market value series for the 27 companies included in the NASDAQ OMX Renewable Energy Gen (GRNREG) index (source: Datastream). 04_Exchange_rates: Exchange rates (source: OECD). 05_LCOE: Average Levelized cost of energy for the United States and Europe (source: IRENA (2022)). 06_PriceLCOE_ratio: Energy prices relative to the levelized cost of energy, where energy prices are pool prices compiled from the Nord Pool power market. 07_Risk_free_and_ERP: (i) 10-year German bond yield and 20-year U.S. bond yield, and (ii) equity risk premium for Europe and U.S. (source: Bloomberg). 08_Unlevered_Betas: Unlevered betas for 23 European firms and 11 North-American firms whose activity is focused on the renewable energy sector (source: S&P Capital IQ).

REFERENCES: IRENA, 2022. Renewable Energy Statistics 2022, available at: https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2022/Jul/IRENA_Renewable_energy_statistics_2022.pdf (accessed 12 May 2024).

Estimated industrial manufacturing agriculture construction and mining energy estimated by North American Industrial Classification System NAICS code county and fuel type for 2014. Additional disaggregation by end use e.g. machine drive process heating facility lighting is provided for manufacturing agriculture and mining industries. Estimation approach is described in detail in the data_foundation folder here https//github.com/NREL/Industry-Energy-Tool/.