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 2023, figures for 2024 are revised provisional.

Changes as of November 2025: Figures have been revised from 2021 – 2022 and updated for 2023 -2024 The revision concerns improved data on (bio)diesel oil consumption by mobile equipment in the construction and services sectors. This results in a shift of biodiesel consumption in energy application transport to energy application heating and cooling. These changes amount to a few PJ.

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 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.

🌍 Global Renewable & Non-Renewable Energy Dataset (IMF) 🌍

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🚀 Overview

This dataset provides comprehensive information on electricity generation and installed capacity across various energy types, both renewable and non-renewable, sourced globally. The data, originally gathered by the International Monetary Fund (IMF), includes detailed insights into ten different energy technologies from countries around the world. The dataset aims to support researchers, data scientists, and energy analysts in exploring trends in global energy production and capacity from 2000 to 2023.

📊 Key Features of the Dataset

• ⚡ Energy Types: Classified into Renewable (e.g., Bioenergy, Hydropower) and Non-Renewable (e.g., Fossil Fuels).
• 📈 Indicators: Provides data on both Electricity Generation and Installed Capacity.
• 🗓️ Time Period: Covers data from 2000 to 2023, allowing for longitudinal analysis of energy trends.
• 💡 Technology Types: Includes energy technologies such as Solar, Wind, Fossil Fuels, and Nuclear.
• 🌎 Countries and Regions: Data covers multiple countries and regions, providing global insights.

🌿 Data Context and Sources

The data originates from publicly available reports and datasets from the International Renewable Energy Agency (IRENA) and the International Monetary Fund (IMF). These sources were meticulously combined to provide a cohesive dataset focused on both renewable and non-renewable energy sectors. The primary motivation behind creating this dataset was to provide a global perspective on energy trends, enabling the exploration of how different regions are progressing toward renewable energy adoption while still relying on traditional energy sources.

📚 Data Sources

• IMF: Data on economic trends in energy production and investment.
• IRENA: Information on renewable energy installed capacity and generation, sourced from comprehensive reports.

This dataset is ideal for tracking changes in energy composition, assessing progress toward sustainable energy goals, and performing cross-country energy comparisons.

🛠️ Dataset Structure

⚙️ How to Use This Dataset

1. 🔍 Energy Transition Analysis: Track the transition of individual countries or regions from non-renewable to renewable energy sources.
2. 🤖 Forecasting Models: Utilize the dataset to build machine learning models to forecast energy trends or capacity needs for the future.
3. 🌐 Cross-Country Comparisons: Compare the energy generation profiles and installed capacity of different countries to understand their energy landscape.
4. 💡 Technology-Specific Insights: Gain insights into the adoption rate and growth of different technologies like wind, solar, and fossil fuels.

🌟 Inspiration Behind This Dataset

This dataset was inspired by the need to address climate change and evaluate how the world is moving towards renewable energy sources. By providing a clear picture of the balance between renewable and non-renewable energy, this dataset can help policymakers, researchers, and advocates better understand the dynamics of global energy supply.

📈 Potential Applications

• 📊 Data Visualization: Create interactive visualizations to explore energy trends globally.
• 🏛️ Policy Assessment: Assist governments and NGOs in assessing their progress toward renewable energy goals.
• 📚 Educational Use: Serve as a resource for students and teachers to understand global energy trends.
• 🌿 Renewable Energy Advocacy: Provide compelling evidence to support the transition to renewable energy.

📌 Limitations and Considerations

• ⚠️ Data Gaps: There may be some missing values for certain years or countries due to unavailability of data.
• 📅 Currency and Updates: The dataset is up to 2023. Regular updates are recommended for continued relevance.
• 📊 Data Source Dependence: Data is derived from official sources; hence, it depends on their data accuracy. ...

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

This data set is time series electricity use data from rural households using off-grid energy systems in Kenya. As well as indicating lighting electricity use for a real-world use case, it can give insight into active occupancy times in the mornings and evenings. This can support estimation of load profiles for higher tiers of the Multi-tier Framework for energy access by adding in load profiles for additional appliances.

Two solar nano-grids (SONGs) were built in two rural communities in Kenya, as part of the Solar Nano-grids project (EPSRC ref: EP/L002612/1). One aspect of the SONGs were battery-charging systems, in which batteries could be charged at a central solar hub, and used in households to power lighting and mobile phone charging. For each battery the electricity use was recorded in real-time between July 2016 and November 2016 inclusive.

The data consist of separate demand (use of battery in the home for lighting) and charging (charging at the central hub) profiles in csv files, individually for each household. The data are half-hourly measurements of average power used for the household lighting system (3 3W LED bulbs with wiring and switches). There is data for 51 households, ranging in length from 3 days to 5 months. Note that the data set is solely electricity use for the household lighting system, and does not include electricity use via the USB port that was present for charging mobile phones. The households are anonymised and are numbered in order of ascending number of days of data.

The household battery packs were Li-ion with capacity 62 Wh, and the data were recorded using a FRDM K-64F mbed embedded in each. 13 post-processing steps were required to process the data gathered in raw form from the batteries into energy profiles for individual households (see reference below). These included: correcting the timestamps caused by time drift or recalibration of the RTCs, attributing batteries to the correct household, addressing logging disruptions and inconsistent logging frequencies, imposing limits on power and duration of use to remove non-representative battery use, and testing loading conditions to remove abnormal energy use. The gaps in the data and varying lengths of the data are caused by: technical challenges with the batteries, meaning that they required frequent repairing; issues with the RTC on the microcontroller being reset; difficulty in attributing data to the correct household. Between 18th July - 1st August (approx.), the charging hub was shut down and so there is a gap in all energy profiles.

Graphical representations of the data for each household, and further information about the solar nano-grids project, the energy data, and the processing steps involved, can be found in Clements, A F. Data-driven approaches enabling the design of community energy systems in the Global South. DPhil Thesis. Department of Engineering Science, University of Oxford. 2019.

These datasets, namely .csv, are snapshots of the regional datasets published by the U.S. Energy Information Administration (EIA) between July 1, 2018 and June 30, 2023. EIA publishes hourly operational data across the United States electricity grid, including demand, net generation of electricity from various sources (such as coal, natural gas, solar), CO2 emissions, import/export to other regions, and many more. The complete details of the EIA-930 data is available here: https://www.eia.gov/electricity/gridmonitor/about. Furthermore, we obtained the solar capacities of each year and each region from EIA (https://www.eia.gov/electricity/data/state/) and had stored the information in the file solar_capacity_factor.csv.

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

Other-Non-Cash-Items Time Series for Renew Energy Global PLC. ReNew Energy Global Plc, together with its subsidiaries, engages in the generation of power through non-conventional and renewable energy sources in India. It operates through five segments: Wind Power, Solar Power, Hydro Power, Transmission Line, and Manufacturing segments. The company develops and owns utility scale wind and solar energy projects, corporate wind and solar energy projects, and utility-scale firm power projects. It also provides operation and maintenance services; consultancy services; and engineering, procurement, and construction services. As of May 31, 2025, the company operates a 18.46 GWs total capacity of clean energy portfolio. ReNew Energy Global Plc was founded in 2011 and is based in London, the United Kingdom.

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

Goodwill Time Series for Renew Energy Global PLC. ReNew Energy Global Plc, together with its subsidiaries, engages in the generation of power through non-conventional and renewable energy sources in India. It operates through five segments: Wind Power, Solar Power, Hydro Power, Transmission Line, and Manufacturing segments. The company develops and owns utility scale wind and solar energy projects, corporate wind and solar energy projects, and utility-scale firm power projects. It also provides operation and maintenance services; consultancy services; and engineering, procurement, and construction services. As of May 31, 2025, the company operates a 18.46 GWs total capacity of clean energy portfolio. ReNew Energy Global Plc was founded in 2011 and is based in London, the United Kingdom.

Selling-General-and-Administrative Time Series for Renew Energy Global PLC. ReNew Energy Global Plc, together with its subsidiaries, engages in the generation of power through non-conventional and renewable energy sources in India. It operates through five segments: Wind Power, Solar Power, Hydro Power, Transmission Line, and Manufacturing segments. The company develops and owns utility scale wind and solar energy projects, corporate wind and solar energy projects, and utility-scale firm power projects. It also provides operation and maintenance services; consultancy services; and engineering, procurement, and construction services. As of May 31, 2025, the company operates a 18.46 GWs total capacity of clean energy portfolio. ReNew Energy Global Plc was founded in 2011 and is based in London, the United Kingdom.

This GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include: GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows: Rooftop Solar: Solar arrays located on rooftops of large buildings. Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots. Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints. Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published. This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints. Metadata: (1) CBI Solar FootprintsAbstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:https://www.eia.gov/maps/layer_info-m.cfmThere were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy

The following submission includes processed laboratory data from NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of MATLAB workspaces. This dataset was created using NREL's Large Amplitude Motion Platform (LAMP) and collected between August and September 2023. Included with this submission is a test log of all the processed data "HERO WEC LAMP test run log.xlsx" so that the user can easily find the data of interest. Additionally, more detailed descriptions of the type of data and how it was processed, or calculated, can be found in the document titled "Lamp Data Description.docx". The MATLAB workspaces can be visualized using the file "LAMP_Data_Viewer_Ver2.m/mlx". The user simply needs to upload the workspace of interest and run the file "LAMP_Data_Viewer_Ver2.m/mlx". Both the .m and .mlx file format has been provided depending on the user's preference.

The MATLAB workspaces have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type:

- DW - Deep water sine wave profile (not run in drivetrain configuration)
- Heave - Heave only sine wave profile
- Heave_NoRO (hydraulic configuration only)
- Heave_ACC (hydraulic configuration only)
- IR - Surge and heave irregular wave profile (not run in drivetrain configuration)
- RW - Heave only profile created from real world encoder data (not run in drivetrain configuration)

For those interested in the raw, unprocessed, data the authors have created a separate submission, linked below. This submission includes the raw TDMS files and associated files necessary to translate the data into either python or MATLAB formats.

This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

This submission contains the original, unprocessed data from the 2023 Large Amplitude Motion Platform (LAMP) testing of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This data serves as a companion to MHKDR #520. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in TDMS format. Specifications of TDMS files can be found on the NI website.

The TDMS files have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type: - DW - Deep water (monochromatic sine wave) profile (not run in drivetrain configuration) - Heave - Heave only (monochromatic sine wave) profile - Heave_NoRO (hydraulic configuration only) - Heave_ACC (hydraulic configuration only) - IR - Surge and heave irregular wave profile (not run in drivetrain configuration) - RW - Heave only profile created from real world encoder data (not run in drivetrain configuration)

Reference documents: - "HERO WEC Lamp Test Run Log.xlsx": contains specifications for each test run - "Lamp Data Description.docx": provides detailed information about data types and processing methods

For those interested in the processed data the authors have created a separate submission, MHKDR #520, linked below.

This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

1.National/Regional Policies 1.1 Paris Agreement Ratification I believe Paris Agreement has fostered the ambition of the countries to revise and implement their climate goals. However, not all of them have ratified it. I think the countries which have not ratified the agreement might show a different direction/ or they might be less ambitious in putting climate change adaptation and mitigation into their political agenda/processes/actions.

Data Source: https://treaties.un.org/Pages/ViewDetails.aspx?src=TREATY&mtdsg_no=XXVII-7-d&chapter=27&clang=_en

1.2 Climate Change ambitions While the majority of countries have promised to take action against climate change through the Paris Agreement, not all of them are working towards reaching the 1.5C goal at the same level. Climate tracker is an organization tracking the “ambition level” and progress of the countries which I believe could be a fruitful source of data.

Overview: https://climateactiontracker.org/publications/paris-agreement-benchmarks/ Data Source: https://climateactiontracker.org/data-portal/

1.3 Carbon Pricing Some countries/regions implement carbon pricing mechanism which is proven to be an efficient mechanism for decreasing carbon emissions. Worldbank provides a dashboard with carbon pricing data and information about the countries. Overview: https://carbonpricingdashboard.worldbank.org/ Data source: https://carbonpricingdashboard.worldbank.org/map_data

2. Economy 2.1 Composition of the sectors I know it is already shared by others, but the World Bank also provides further information on countries’ economy structures. One thing that I believe could be useful further to the GDP is the sector composition of the country which could play a role in countries' emission reduction. While it is easier for services to reach net-zero, it is harder for manufacturing. (this is also valid for companies, it is much easier to reach net-zero emission for a service company, but it could be very difficult for a steel production/processing plant to be emission-free). Overview: https://data.worldbank.org/indicator/NV.IND.MANF.ZS Data source: http://wdi.worldbank.org/table/4.2

2.2 Innovation Index Combating climate change requires fundamental changes in the systems that we have been living. Thus, innovation (technological, business model, political, social…) is necessary at all levels. Therefore, I believe the Global Innovation Index (GII) can be used as a proxy to measure innovative activities. Overview: https://www.globalinnovationindex.org/home Data source: https://www.globalinnovationindex.org/analysis-indicator

3. Low carbon Technologies Development, production and adoption of clean energy technologies are vital for lower carbon transitions. While latest developments in solar technologies made it both the cheapest and clean energy source, there is still a long way to reach a “reliable” technology to be considered as a commercially feasible option for Carbon Capture and Storage. IEA provides information related to low carbon RDDs, but it has limited country data (Mostly OECD countries). Overview: https://www.iea.org/fuels-and-technologies Data Source: https://www.iea.org/reports/energy-technology-rdd-budgets-2020

4. Development & Just Transitions 4.1 Energy Access Today there are still millions of people who don’t have access to electricity and clean cooking. Although for some countries finding ways to decrease emissions, for some others to ensure their population’s “reliable affordable and clean energy access” (SDG 7, UNDP) is the challenge. The World Bank provides data on Electricity production, sources, and percentage of the population who has access to electricity by country as part of World Development Indicators. Overview: https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS Data Source: http://wdi.worldbank.org/table/3.7

4.2. Bonus: Environmental Justice (no dataset uploaded- just qualitative data) Environmental Justice Atlas is a citizen-led mapping tool which shows conflicts related to environmental injustices. The data cannot be fully downloaded and subject to restrictive data use, and I am not sure even if it could be quantified. But, I believe it could be useful to think about the social aspects of transitions. https://ejatlas.org/

Price-To-Tangible-Book-Ratio Time Series for Renew Energy Global PLC. ReNew Energy Global Plc, together with its subsidiaries, engages in the generation of power through non-conventional and renewable energy sources in India. It operates through five segments: Wind Power, Solar Power, Hydro Power, Transmission Line, and Manufacturing segments. The company develops and owns utility scale wind and solar energy projects, corporate wind and solar energy projects, and utility-scale firm power projects. It also provides operation and maintenance services; consultancy services; and engineering, procurement, and construction services. As of May 31, 2025, the company operates a 18.46 GWs total capacity of clean energy portfolio. ReNew Energy Global Plc was founded in 2011 and is based in London, the United Kingdom.

2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction Study The 2023 University of Puerto Rico at Mayagüez National Institute for Congestion Reduction (NCIR) Study provided insight into the travel patterns and associated energy consumption of participants. Study results helped researchers identify opportunities for the development of policies that could incentivize the use of alternative modes of travel such as transit and micromobility. Such travel modes reduce congestion by reducing the miles traveled by privately owned vehicles in urban and rural areas. The National Institute for Congestion Reduction provides multimodal congestion reduction strategies through real-world deployments that leverage advances in technology, big data science, and innovative transportation options to optimize the efficiency and reliability of the transportation system for all users. ## Data Collection Agency The University of Puerto Rico at Mayagüez conducted the study. ## Survey Methodology The study was conducted in Spanish. Data collection was enabled via the open-source NREL OpenPATH platform. The resulting dataset consists of partially automated travel diaries—combining sensed and surveyed data reflecting patterns of multimodal, end-to-end, individual human mobility—as well as demographic and socioeconomic information from the 17 participants. ## Survey Records, Data, and Documentation Study records include 17 participants.more » The total number of trips was 458 and total non-air-miles traveled was approximately 1,469.« less