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

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.

Displays several units of energy consumption for households, businesses, and industries in the City of Chicago during 2010. Electric The data was aggregated from ComEd and Peoples Natural Gas by Accenture. Electrical and gas usage data comprises 88 percent of Chicago's buildings in 2010. The electricity data comprises 68 percent of overall electrical usage in the city while gas data comprises 81 percent of all gas consumption in Chicago for 2010. Census blocks with less than 4 accounts is displayed at the Community Area without further geographic identifiers. This dataset also contains selected variables describing selected characteristics of the Census block population, physical housing, and occupancy.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale utility-reported energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities under its regulation to develop and report community energy use data to the UER.This dataset includes electricity and natural gas usage data reported at the city, town, and village level collected under a data protocol in effect between 2016 and 2021. Other UER datasets include energy use data reported at the county and ZIP code level. Data collected after 2021 were collected according to a modified protocol. Those data may be found at https://data.ny.gov/Energy-Environment/Utility-Energy-Registry-Monthly-Community-Energy-U/4txm-py4p.Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld.

China is the largest consumer of primary energy in the world, having used some 170.7 exajoules in 2023. This is a lot more than what the United States consumed, which comes in second place. The majority of primary energy fuels worldwide are still derived from fossil fuels, such as oil and coal. China's energy mix China’s primary energy mix has shifted from a dominant use of coal to an increase in natural gas and renewable sources. Since 2013, the renewables share in total energy consumption has grown by around eight percentage points. Overall, global primary energy consumption has increased over the last decade, and it is expected to experience the largest growth in emerging economies like the BRIC countries - Brazil, Russia, India, and China. What is primary energy? Primary energy is the energy inherent in natural resources such as crude oil, coal, and wind before further transformation. For example, crude oil can be refined into secondary fuels, such as gasoline or diesel, while wind is harnessed for electricity - itself a secondary energy source. A country’s total primary energy supply is a measure of the country’s primary energy sources. Meanwhile, end use energy is the energy directly consumed by the user and includes primary fuels such as natural gas, as well as secondary sources, like electricity and gasoline.

Regulatory Indicators for Sustainable Energy (RISE) is a comprehensive policy scorecard assessing the investment climate for sustainable energy and focusing on three key areas: energy access, energy efficiency and renewable energy. RISE covers 111 countries across the developed and developing worlds, which together represent over 90% of global population, GDP and energy consumption. With 28 indicators, 85 sub-indicators and 158 data points per country, RISE helps policy makers to understand how they are doing, compare across countries, learn from peer groups, and identify priority actions for the future. The source data and documents for 111 countries are available at http://rise.worldbank.org/library To learn more, please visit http://rise.worldbank.org/

Consumption of renewable energy in the United States has experienced a continual annual increase since 1998. In 2023, renewables consumption in the North American country peaked at nearly 11 exajoules. This represented a growth of roughly two percent when compared to the figure reported the previous year.

RISE is a set of indicators to help compare national policy and regulatory frameworks for sustainable energy. It assesses countries’ policy and regulatory support for each of the three pillars of sustainable energy—access to modern energy, energy efficiency, and renewable energy.

The global renewable power support policy dataset was compiled by Sarah Hafner (Anglia Ruskin University, United Kingdom) and Johan Lilliestam (Institute for Advanced Sustainability Studies (IASS), Germany) in February-July 2017 and completed during 2017. The work was led by Johan Lilliestam but each author gathered half of the data. The data was formatted and checked for internal consistency by Tim Tröndle, IASS.

All non-commercial users are allowed to use and manipulate our data, but are required to give appropriate attribution. Hence, please cite this data as:

Hafner, S. & Lilliestam, J. (2019): The global renewable power support dataset. Institute for Advanced Sustainability Studies (IASS) & Anglia Ruskin University, Potsdam & Cambridge. Doi: https://doi.org/ 10.5281/zenodo.3371375.

If you are interested in contributing to and further developing the dataset: please contact Johan Lilliestam (IASS Potsdam).

The search was done in publically available sources, including but not limited to the IEA renewables policy database, res-legal.eu, Worldbank data, as well as data from the responsible national ministries.

Our data holds information on 10 specific policy instruments explicitly dedicated to the support for expansion of renewable electricity generation 1990-2016; some instruments, including taxation of non-renewables or emission trading, affect other sectors than renewable power, but are mentioned in their original policy description to also be dedicated to increasing renewable power. Our data concerns national policy measures, but ignores policies enacted on higher (e.g. EU-level in Europe) or lower (e.g. state-level policies in Canada, USA) political levels. For example, the “no support” entry for the United Arab Emirates indicates that there were no national-level policies: all policies were, in this case, emirate-specific.

The data exists in two versions: one version readable for humans (RE_policies_fullglobal.xlsx) and for each instrument type as .csv. The information in the two versions is identical and differs only in the way it is displayed.

Please refer to the metadata file for a detailed description of the dataset and the data categories.

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network "shapes", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects.

BUTTER-E is intended to be joined with the BUTTER dataset (see "BUTTER - Empirical Deep Learning Dataset on OEDI" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.

The Utility Energy Registry (UER) is a database platform that provides streamlined public access to aggregated community-scale energy data. The UER is intended to promote and facilitate community-based energy planning and energy use awareness and engagement. On April 19, 2018, the New York State Public Service Commission (PSC) issued the Order Adopting the Utility Energy Registry under regulatory CASE 17-M-0315. The order requires utilities and CCA administrators under its regulation to develop and report community energy use data to the UER. This dataset includes electricity and natural gas usage data reported by utilities at the county level. Other UER datasets include energy use data reported at the city, town, and village, and ZIP code level. Data in the UER can be used for several important purposes such as planning community energy programs, developing community greenhouse gas emissions inventories, and relating how certain energy projects and policies may affect a particular community. It is important to note that the data are subject to privacy screening and fields that fail the privacy screen are withheld. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

United States Energy Consumption: Residential: Primary: Renewable Energy (RE) data was reported at 55.393 BTU tn in Apr 2018. This records an increase from the previous number of 54.115 BTU tn for Mar 2018. United States Energy Consumption: Residential: Primary: Renewable Energy (RE) data is updated monthly, averaging 48.502 BTU tn from Jan 1973 (Median) to Apr 2018, with 544 observations. The data reached an all-time high of 85.781 BTU tn in Dec 1985 and a record low of 27.164 BTU tn in Feb 1973. United States Energy Consumption: Residential: Primary: Renewable Energy (RE) data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.RB002: Energy Consumption.

In 2022, fossil fuels accounted for a little less than half of NextEra Energy's owned electricity generation mix. Nuclear power represented a share of 22 percent, while renewable sources (namely solar and wind) contributed with a 32 percent. With an installed renewable capacity of more than 26 gigawatts, U.S.-based NextEra Energy is one of the largest renewable energy producers in the world.

Graph and download economic data for Manufacturing Sector: Energy Input (MPU9900553) from 1988 to 2023 about energy, sector, manufacturing, rate, and USA.

PUDL v2024.10.0 Data Release

This is a special early release to publish the new VCE Resource Adequacy Renewable Energy (RARE) dataset. It also includes final releases of EIA 860 and 923 data for 2023 and the FERC Form 714 data for 2021-2023, which had previously been integrated from the XBRL data published by FERC. See the release notes for more narrative detail.

New Data

Vibrant Clean Energy Resource Adequacy Renewable Energy (RARE) Power Dataset

• Integrate the VCE hourly capacity factor data for solar PV, onshore wind, and offshore wind from 2019 through 2023. The data in this table were produced by Vibrant Clean Energy, and are licensed to the public under the Creative Commons Attribution 4.0 International license (CC-BY-4.0). This data complements the WECC-wide GridPath RA Toolkit data currently incorporated into PUDL, providing capacity factor data nation-wide with a different set of modeling assumptions and a different granularity for the aggregation of outputs. See GridPath Resource Adequacy Toolkit Data and Vibrant Clean Energy Resource Adequacy Renewable Energy (RARE) Power Dataset for more information. See #3872.

New Data Coverage

EIA 860

• Added EIA 860 final release data from 2023. See #3684 and PR #3871.

EIA 923

• Added EIA 923 final release data from 2023 and revised data from 2022. See #3902 and PR #3903.

FERC Form 714

• Integrated 2021-2023 years of the FERC Form 714 data. FERC updated its reporting format for 2021 from a CSV files to XBRL files. This update integrates the two raw data sources and extends the data coverage through 2023. See #3809 and #3842.

Schema Changes

• Added out_eia_yearly_assn_plant_parts_plant_gen table. This table associates records from the out_eia_yearly_plant_parts with plant_gen records from that same plant parts table. See issue #3773 and PR #3774.

Bug Fixes

• Included more retiring generators in the net generation and fuel consumption allocation. Thanks to @grgmiller for this contirbution #3690.

• Fixed a bug found in the rolling averages used to impute missing values in fuel_cost_per_mmbtu and to calculate capex_annual_addition_rolling. Thanks to RMI for identifying this bug! See issue #3889 and PR #3892.

Major Dependency Updates

• Updated to use Numpy v2.0 and Splink v4.0. See issues #3736, #3735 and PRs #3547, #3834.

Quality of Life Improvements

• We now use an asset factory to generate Dagster assets for near-identical FERC1 output tables. See #3147 and #3883. Thanks to @hfireborn and @denimalpaca for their work on this one!

Other PUDL v2024.10.0 Resources

• PUDL v2024.10.0 Data Dictionary
• PUDL v2024.10.0 Documentation
• PUDL in the AWS Open Data Registry
• PUDL v2024.10.0 in a free, public AWS S3 bucket: s3://pudl.catalyst.coop/v2024.10.0/
• PUDL v2024.10.0 in a requester-pays GCS bucket: gs://pudl.catalyst.coop/v2024.10.0/
• Zenodo archive of the PUDL GitHub repo for this release
• PUDL v2024.10.0 release on GitHub
• PUDL v2024.10.0 package in the Python Package Index (PyPI)

Contact Us

If you're using PUDL, we would love to hear from you! Even if it's just a note to let us know that you exist, and how you're using the software or data. Here's a bunch of different ways to get in touch:

• Follow us on GitHub
• Use the PUDL Github issue tracker to let us know about any bugs or data issues you encounter
• GitHub Discussions is where we provide user support.
• Watch our GitHub Project to see what we're working on.
• Email us at hello@catalyst.coop for private communications.
• On Mastodon: @CatalystCoop@mastodon.energy
• On BlueSky: @catalyst.coop
• On Twitter: @CatalystCoop
• Connect with us on LinkedIn
• Play with our data and notebooks on Kaggle
• Combine our data with ML models on HuggingFace
• Learn more about us on our website: https://catalyst.coop
• Subscribe to our announcements list for email updates.

Energy consumption worldwide presented a trend of growth, reaching 637.8 quadrillion British thermal units in 2022. Figures are expected to rise in the following years, and peak at over 854 quadrillion British units in 2050.

CN: Electricity Production: Solar: YoY: Henan data was reported at 9.800 % in Dec 2024. This records an increase from the previous number of -8.700 % for Nov 2024. CN: Electricity Production: Solar: YoY: Henan data is updated monthly, averaging 9.450 % from May 2016 (Median) to Dec 2024, with 88 observations. The data reached an all-time high of 463.700 % in Dec 2016 and a record low of -36.600 % in Apr 2023. CN: Electricity Production: Solar: YoY: Henan data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Energy Sector – Table CN.RBA: Energy Production: Electricity: Solar.

The indicator results from the division of the gross domestic product (GDP) by the gross available energy for a given calendar year. It measures the productivity of energy consumption and provides a picture of the degree of decoupling of energy use from growth in GDP. For the calculation of energy productivity Eurostat uses the GDP either in the unit of million euro in chain-linked volumes to the reference year 2010 (at 2010 exchange rates) or in the unit million purchasing power standards (PPS). The unit euro in chain linked volumes allows observing the energy productivity trends over time in a single geographic area, whereas the unit PPS allows comparison between countries for the same year. The gross available energy is calculated as: Primary production + Recovered & recycled products + Imports – Exports + Stock changes.

The New York State Energy Research and Development Authority (NYSERDA) hosts a web-based Distributed Energy Resources (DER) integrated data system at https://der.nyserda.ny.gov/. This site provides information on DERs that are funded by and report performance data to NYSERDA. Information is incorporated on more diverse DER technology as it becomes available. Distributed energy resources (DER) are technologies that generate or manage the demand of electricity at different points of the grid, such as at homes and businesses, instead of exclusively at power plants, and includes Combined Heat and Power (CHP) Systems, Anaerobic Digester Gas (ADG)-to-Electricity Systems, Fuel Cell Systems, Energy Storage Systems, and Large Photovoltaic (PV) Solar Electric Systems (larger than 50 kW). Historical databases with hourly readings for each system are updated each night to include data from the previous day. The web interface allows users to view, plot, analyze, and download performance data from one or several different DER sites. Energy storage systems include all operational systems in New York including projects not funded by NYSERDA. Only NYSERDA-funded energy storage systems will have performance data available. The database is intended to provide detailed, accurate performance data that can be used by potential users, developers, and other stakeholders to understand the real-world performance of these technologies. For NYSERDA’s performance-based programs, these data provide the basis for incentive payments to these sites. How does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on Twitter, Facebook, YouTube, or Instagram.