The “Sustainable Energy for all (SE4ALL)” initiative, launched in 2010 by the UN Secretary General, established three global objectives to be accomplished by 2030: to ensure universal access to modern energy services, to double the global rate of improvement in global energy efficiency, and to double the share of renewable energy in the global energy mix. SE4ALL database supports this initiative and provides country level historical data for access to electricity and non-solid fuel; share of renewable energy in total final energy consumption by technology; and energy intensity rate of improvement

Information on grid-connected energy storage projects and relevant state and federal policies

The DOE Global Energy Storage Database provides research-grade information on grid-connected energy storage projects and relevant state and federal policies. All data can be exported to Excel or JSON format. As of September 22, 2023, this page serves as the official hub for The Global Energy Storage Database.

We present the full database of the article "Explainable Supervised Machine Learning Model to Predict Solvation Free Energy".

This is the database used for a ML model, containing a variety of solvent-solute pairs with known experimental solvation free energy ΔG_solv values. Data entries were collected from two separate databases. The FreeSolv library, with 642 experimental aqueous ΔG_solv determinations and the Solv@TUM database with 5597 entries for non-aqueous solvents. Both databases were selected given their wide-scale of solute/solvents pairs, amassing 6239 experimental values across light and heavy-atom solutes with a diverse solvent structure and with small value uncertainties.

Experimental ΔG_solv values range from -14 to 4 kcal mol^-1 and each solute/solvent pair is represented by their chemical family, SMILES string and InChlKey. We generated 213 chemical descriptors for every solvent and solute in each entry using RDKit software, version 2022.09.4, running on top of Python 3.9. Descriptors were calculated from the “MolFromSmiles” function in “RDKIT.Chem” as descriptors with non-numerical values were removed. The descriptors encode significant chemical information and are used to present physicochemical characteristics of compounds, building a relationship between structure and ΔG_solv.

Through Machine Learning regression algorithms, our models were able to make ΔG_solv predictions with high accuracy, based on the information encoded in each chemical feature.

The 2005 edition of the Energy Statistics Database contains comprehensive energy statistics on more than 215 countries or areas for production, trade, transformation and intermediate and final consumption (end-use) for primary and secondary conventional, non-conventional and new and renewable sources of energy. In addition, mid-year population estimates are included to enable the computation of per capita data. Data on heating (calorific) values are also provided to enable conversion to a common unit (terajoules) for interfuel comparison and analyses.

The Industrial Assessment Centers (IAC) Database is a collection of all the publicly available data from energy efficiency assessments conducted by IACs at small and medium-sized industrial facilities. The data includes information beginning in 1981 on the type of facility assessed (size, industry, energy usage, etc.) as well as the details of resulting recommendations (type, energy and dollars savings etc.). As of November, 2023, the IAC database contains information on 20,971 assessments and an associated 156,470 recommendations for energy efficiency improvements.

This project provides a national unified database of residential building retrofit measures and associated retail prices and end-user might experience. These data are accessible to software programs that evaluate most cost-effective retrofit measures to improve the energy efficiency of residential buildings and are used in the consumer-facing website https://remdb.nrel.gov/

This publicly accessible, centralized database of retrofit measures offers the following benefits:

• Provides information in a standardized format
• Improves the technical consistency and accuracy of the results of software programs
• Enables experts and stakeholders to view the retrofit information and provide comments to improve data quality
• Supports building science R&D
• Enhances transparency

This database provides full price estimates for many different retrofit measures. For each measure, the database provides a range of prices, as the data for a measure can vary widely across regions, houses, and contractors. Climate, construction, home features, local economy, maturity of a market, and geographic location are some of the factors that may affect the actual price of these measures.

This database is not intended to provide specific cost estimates for a specific project. The cost estimates do not include any rebates or tax incentives that may be available for the measures. Rather, it is meant to help determine which measures may be more cost-effective. The National Renewable Energy Laboratory (NREL) makes every effort to ensure accuracy of the data; however, NREL does not assume any legal liability or responsibility for the accuracy or completeness of the information.

The Building Performance Database (BPD) is the largest publicly-available source of measured energy performance data for buildings in the United States. It contains information about the building's energy use, location, and physical and operational characteristics. The BPD can be used by building owners, operators, architects and engineers to compare a building's energy performance against customized peer groups, identify energy performance opportunities, and set energy performance. It can also be used by energy performance program implementers to analyze energy performance features and trends in the building stock. The BPD compiles data from various data sources, converts it into a standard format, cleanses and quality checks the data, and provides users with access to the data in a way that maintains anonymity for data providers.

The BPD consists of the database itself, a graphical user interface allowing exploration of the data, and an application programming interface allowing the development of third-party applications using the data.

Time-Series Energy Database Market Outlook

According to our latest research, the global Time-Series Energy Database market size reached USD 1.82 billion in 2024, and is expected to grow at a CAGR of 17.2% from 2025 to 2033, culminating in a forecasted market value of USD 8.76 billion by 2033. The primary growth driver is the surging demand for real-time data analytics and advanced grid management solutions, which are essential for optimizing energy distribution, integrating renewable sources, and enhancing operational efficiency across the energy sector.

One of the most significant growth factors for the Time-Series Energy Database market is the rapid digital transformation underway within the global energy industry. As utilities and energy producers increasingly rely on Internet of Things (IoT) devices and smart meters, the volume of time-stamped energy data has grown exponentially. This data influx necessitates robust, scalable databases capable of handling high-velocity data streams, supporting predictive analytics for grid stability, and enabling near real-time decision-making. The integration of artificial intelligence and machine learning with time-series databases further amplifies their value, empowering energy companies to forecast demand, detect anomalies, and optimize asset utilization with unprecedented accuracy.

Another critical driver is the accelerating adoption of renewable energy sources such as solar and wind. These sources introduce variability and intermittency into energy supply, making it imperative for grid operators to monitor and analyze real-time data continuously. Time-series energy databases provide the backbone for managing this complexity, allowing for seamless renewable integration, dynamic load balancing, and improved forecasting accuracy. The increasing government mandates for clean energy transition and emission reductions worldwide are compelling energy producers to invest in advanced data management technologies, thereby boosting the adoption of time-series energy databases.

The proliferation of distributed energy resources (DERs), such as microgrids, battery storage, and electric vehicles, has added further impetus to market growth. Managing these decentralized assets requires granular, time-stamped data to ensure efficient operation and coordination within the broader energy ecosystem. Time-series databases enable utilities and energy producers to aggregate, analyze, and act upon data from diverse sources, supporting innovative business models such as demand response, peer-to-peer energy trading, and real-time pricing. This evolution in the energy landscape is expected to sustain strong demand for time-series energy database solutions over the forecast period.

From a regional perspective, North America leads the Time-Series Energy Database market, driven by early adoption of smart grid technologies and a robust focus on renewable energy integration. Europe follows closely, propelled by stringent regulatory frameworks and ambitious decarbonization targets. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, expanding energy infrastructure, and increasing investments in digitalization. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, supported by modernization initiatives and growing awareness of the benefits of data-driven energy management. Each region presents unique opportunities and challenges, shaping the global competitive landscape.

Component Analysis

The Time-Series Energy Database market by component is segmented into Software, Hardware, and Services. The software segment currently dominates the market, accounting for the largest share due to the critical role of advanced analytics, visualization tools, and data management platforms in processing and interpreting vast volumes of time-series data. Modern software solutions offer seamless integration with existing energy management systems, support for cloud-native architectures, and compatibility with a wide range of IoT devices. As energy companies strive for real-time insights and operational optimization, the demand for sophisticated time-series database software is expected to remain robust throughout the forecast period.

The hardware segment, while smaller in comparison to software, is witnessing steady growth as energy providers invest in high-performance servers, sto

Users can generate reports showing the amount of energy consumed by geographical area, sector (residential, commercial, industrial) classifications. The database also provides easy downloading of energy consumption data into the comma-separated values (CSV) file format.

References contained in this database are from Bibliography on Atomic Energy Levels and Spectra, NBS Special Publication 363 and Supplements, as well as current references since the last published bibliography collected by the NIST Atomic Spectroscopy Data Center (http://www.nist.gov/physlab/div842/grp01/asdc_info.cfm). These references pertain to atomic structure and spectra that arise from interactions or excitations involving electrons in the outer shells of free atoms and atomic ions, or from inner shell excitations corresponding to frequencies up to the soft x-ray range. Please note that this database does not contain references to atomic transition probabilities, line intensities, or broadening. These references can be found in two other bibliographic databases maintained by the same Data Center: NIST Atomic Transition Probability Bibliographic Database (http://physics.nist.gov/fvalbib) and NIST Atomic Spectral Line Broadening Bibliographic Database (http://physics.nist.gov/linebrbib). References to publications containing critically compiled data can be found in a separate database of NIST compilations of atomic spectroscopy data (http://physics.nist.gov/PhysRefData/datarefs/datarefs_search_form.html).

Here, we present a database which collates historical, current, and future cost and performance data and assumptions for gas-fired power generation from the open literature. Natural gas supplies 23% of global electricity, but must be rapidly phased down to meet global decarbonisation objectives. The data are global in scope but with regional and national specificity, covers the years 2015 through to 2050, and span 620 datapoints from 14 sources.

The database enables modellers to select and justify model input data and provides a benchmark for comparing assumptions and projections to other sources across the literature to validate model inputs and outputs. It is designed to be easily updated with new sources of data, ensuring its utility, comprehensiveness, and broad applicability over time.Technoeconomic data on new-build gas-fired power generation was collected from websites, reports, academic articles and databases of national and international organisations.

This database provides an assessment of the Salinity Gradient Energy potential that can be harnessed from river mouths worldwide (also known as blue energy or osmotic energy). This resource integrates data on freshwater discharge, salinity, and temperature at river mouths globally, offering estimates of theoretical and extractable SGE potential alongside additional parameters of interest.

A detailed methodology, maps, and analyses derived from this database are presented in the manuscript: Álvarez-Silva, O., Roldán-Carvajal, M., & Arévalo, F. "Extended assessment of the globally extractable salinity gradient energy from river mouths." (submitted for publication on 13.12.2024). When using this database, please cite the scientific paper and the dataset, prioritizing the scientific publication to ensure proper acknowledgment.

Inputs 1. Sea Surface Salinity: Data from the NASA Soil Moisture Active Passive (SMAP) observatory, specifically the JPL SMAP-SSS V5.0 CAP product. 2. Sea Surface Temperature: Data from the Moderate-resolution Imaging Spectrometer (MODIS) onboard NASA’s Aqua satellite, part of the Earth Observation System (EOS). 3. River Discharge Databases: • Database 1 (N20): Multiannual average discharge (1980–2010) from 10,848 river mouths, derived from global models and satellite data as compiled by Nienhuis et al. (2020) DOI: 10.1038/s41586-019-1905-9. Used for assessing global theoretical SGE potential. • Database 2 (ARA24): Monthly discharge data from 1,078 river mouths, compiled from global datasets and national hydrology agencies. Used for estimating extractable SGE potential.

Outputs 1. SGE_Global_Database_N20.xlsx: A single-sheet dataset with data for 10,848 river mouths, including: • River name, country, and ocean basin. • Coordinates and multiannual average hydrological and oceanographic data. • Energy density, theoretical potential, and regional summaries.

1. SGE_Global_Database_ARA24.xlsx: A multi-sheet dataset with data for 1,078 river mouths, including: • Sheet 1: River names, locations (coordinates, country, region, continent, ocean basin), mean monthly discharge, environmental discharge, extraction factor, design flow, monthly extractable discharge, and capacity factor. • Sheet 2: Monthly energy density variability. • Sheet 3: Monthly variability and long-term averages of theoretical potential. • Sheet 4: Monthly variability and long-term averages of extractable potential. • Sheets 5-7: Monthly extractable potential summaries by ocean basin, country, and region, respectively.

2. SGE_Global_Viewer.exe: • An interactive application compiled in Matlab® to visualize ARA24 data. • Users can adjust extraction factors and environmental discharge, with outputs including design flow, capacity factor, and extractable potential. • The users do not require Matlab® installation or a software license.

Dataset

This dataset was created by Dr. Festus Adedoyin

Released under CC0: Public Domain

Contents

The mission of the High Throughput Experimental Materials Database (HTEM DB) is to enable discovery of new materials with useful properties by releasing large amounts of high-quality experimental data to public. The HTEM DB contains information about materials obtained from high-throughput experiments at the National Renewable Energy Laboratory (NREL).

The “Sustainable Energy for all (SE4ALL)” initiative, launched in 2010 by the UN Secretary General, established three global objectives to be accomplished by 2030: to ensure universal access to modern energy services, to double the global rate of improvement in global energy efficiency, and to double the share of renewable energy in the global energy mix.

SE4ALL database supports this initiative and provides country level historical data for access to electricity and non-solid fuel; share of renewable energy in total final energy consumption by technology; and energy intensity rate of improvement

A 2.5-year campus-level smart meter database with equipment data for energy analytics

https://doi.org/10.5061/dryad.k3j9kd5h6

General Information

1. Description: Accurate campus electricity management is crucial for energy efficiency and optimization. However, comprehensive load data is often lacking, hindering research efforts. This paper presents a dataset from HKUST, featuring over 1,400 meters across more than 20 buildings, collected over 2.5 years. The raw data was refined for research using the Brick Schema, enabling studies in load pattern recognition, fault detection, demand response strategies, and load forecasting.
2. Date of data collection: 2022-01-01 to 2024-05-27.
3. Geographic data collection location: Sai Kung District, Hong Kong, China(22.3363°N 114.2634°E).
4. Goal: This dataset is tailored for users engaged in energy management and analysis, featuring detailed internal equipment data. It supports applications in patte...

Preventing energy consumption in emergencies such as the last COVID-19 pandemic can ensure the continued operation of hospitals and food supply centres. In addition, considering the relationship of energy consumption with various factors generates points of attention. This work is focused on presenting the prediction of energy consumption in Mexico using data related to environmental, economic and energy aspects recorded from 1965 to 2021. The input variables were: year, carbon dioxide emissions, Gross Domestic Product per capita, number of power plants, increase in temperature in the world and oil production. The models of Artificial Neural Networks (ANN’s) based on a single layer and hidden multilayer obtained a good correlation between the real values and the simulated ones with a coefficient of determination (R2) of 0.9999 and Mean Absolute Percentage Error (MAPE) of 0.37%. For prediction, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) model generated a correlation with an R2 of 0.8910 between the real and forecast data. The data demonstrated that ANN-based models are a tool capable of predicting energy consumption to support decision-making on the distribution and consumption of energy resources in the face of future emergencies.

The R&I Workforce in Energy Transition Survey (RIWET) Database has been produced in the context of the gEneSys Project.

It contains the data collected through an online survey targeting researchers, technicians, experts, academics, practitioners engaged in scientific and technological knowledge production for the energy sector. The survey was designed to gauge employees' overall job satisfaction and assess the organizational culture, work-related quality of life and collaboration dynamics. The questionnaire explored persistent gender inequalities in the energy labor force and the strategies to address them from the workers' perspectives.

The survey builds upon the recent study commissioned by CINEA, European Climate, Infrastructure and Environment Executive Agency, to help the European Commission improve the role of women in the energy transition and has been distributed to employees or professionals working in R&I activities in any energy related field (generation, storage, management or distribution, manufacturing, servicing, or distributing) of public or private organizations including research centres/academic departments, national/local agencies, companies, and businesses.

As part of work package nº2 of the H2020 PROSEU project, which aimed to establish a baseline review and characterisation of renewable energy sources (RES) prosumer (self-consumption) initiatives across Europe, databases identifying the diversity of collective forms of RES prosumers and related stakeholders were built by the project partners using the templates and respective variables presented here (English language). The databases served to create a stratified sample of RES prosumer initiatives for purposes of a survey, as well as distinguish them from other stakeholders in the field.