All Projects → owid → energy-data

owid / energy-data

Licence: other
Data on energy by Our World in Data

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to energy-data

SpineOpt.jl
A highly adaptable modelling framework for multi-energy systems
Stars: ✭ 25 (-82.01%)
Mutual labels:  energy, electricity
open-energy-view
View resource consumption trends, history, analysis, and insights.
Stars: ✭ 32 (-76.98%)
Mutual labels:  energy, electricity
PowerSimulations.jl
Julia for optimization simulation and modeling of PowerSystems. Part of the Scalable Integrated Infrastructure Planning Initiative at the National Renewable Energy Lab.
Stars: ✭ 202 (+45.32%)
Mutual labels:  energy, electricity
Scaphandre
⚡ Electrical power consumption metrology agent. Let scaph dive and bring back the metrics that will help you make your systems and applications more sustainable !
Stars: ✭ 246 (+76.98%)
Mutual labels:  energy
Reactor-and-Turbine-control-program
This is my Reactor- and Turbine control program for ComputerCraft and BigReactors
Stars: ✭ 18 (-87.05%)
Mutual labels:  energy
ioBroker.tado
Tado cloud connector to control Tado devices
Stars: ✭ 25 (-82.01%)
Mutual labels:  energy
ioBroker.sourceanalytix
Detailed analysis of your Energy, gas and liquid consumptions
Stars: ✭ 61 (-56.12%)
Mutual labels:  energy
tuyapower
Python module to read status and energy monitoring data from Tuya based WiFi smart devices. This includes state (on/off), current (mA), voltage (V), and power (wattage).
Stars: ✭ 101 (-27.34%)
Mutual labels:  energy
Oemof Solph
A model generator for energy system modelling and optimisation (LP/MILP).
Stars: ✭ 176 (+26.62%)
Mutual labels:  energy
Energy-Calculator
🌏 Simple Energy-Calculator Script In Python
Stars: ✭ 30 (-78.42%)
Mutual labels:  energy
antaresViz
ANTARES Visualizations
Stars: ✭ 19 (-86.33%)
Mutual labels:  energy
energy
energy package for R
Stars: ✭ 36 (-74.1%)
Mutual labels:  energy
ontology
Repository for the Open Energy Ontology (OEO)
Stars: ✭ 71 (-48.92%)
Mutual labels:  energy
AMO-Tools-Suite
AMO-Tools-Suite is an energy efficiency calculation library in C++ with optional Nan Node add-on bindings for the Department of Energy Advanced Manufacturing Office (DOE AMO) Desktop, also known as MEASUR.
Stars: ✭ 16 (-88.49%)
Mutual labels:  energy
OpenESS
KETI Data Platform : OpenESS(Energy Storage System)
Stars: ✭ 19 (-86.33%)
Mutual labels:  energy
HPC
A collection of various resources, examples, and executables for the general NREL HPC user community's benefit. Use the following website for accessing documentation.
Stars: ✭ 64 (-53.96%)
Mutual labels:  energy
Pudl
The Public Utility Data Liberation Project
Stars: ✭ 200 (+43.88%)
Mutual labels:  energy
flexmeasures
The intelligent & developer-friendly EMS to support real-time energy flexibility apps, rapidly and scalable.
Stars: ✭ 79 (-43.17%)
Mutual labels:  energy
comparison groups
Repository for discussion of Comparison Group topics
Stars: ✭ 22 (-84.17%)
Mutual labels:  energy
learnergy
💡 Learnergy is a Python library for energy-based machine learning models.
Stars: ✭ 57 (-58.99%)
Mutual labels:  energy

Data on Energy by Our World in Data

Our complete Energy dataset is a collection of key metrics maintained by Our World in Data. It is updated regularly and includes data on energy consumption (primary energy, per capita, and growth rates), energy mix, electricity mix and other relevant metrics.

The complete Our World in Data Energy dataset

🗂️ Download our complete Energy dataset : CSV | XLSX | JSON

The CSV and XLSX files follow a format of 1 row per location and year. The JSON version is split by country, with an array of yearly records.

The variables represent all of our main data related to energy consumption, energy mix, electricity mix as well as other variables of potential interest.

We will continue to publish updated data on energy as it becomes available. Most metrics are published on an annual basis.

A full codebook is made available, with a description and source for each variable in the dataset.

Our source data and code

The dataset is built upon a number of datasets and processing steps:

Additionally, to construct variables per capita and per GDP, we use the following datasets and processing steps:

Changelog

  • On August 9, 2022:
    • All inconsistencies due to different definitions of regions among different datasets (especially Europe) have been fixed.
      • Now all regions follow Our World in Data's definitions.
      • We also include data for regions as defined in the original datasets; for example, Europe (BP) corresponds to Europe as defined by BP.
    • All data processing now occurs outside this repository; the code has been migrated to be part of the etl repository.
    • Variable fossil_cons_per_capita has been renamed fossil_elec_per_capita for consistency, since it corresponds to electricity generation.
    • The codebook has been updated following these changes.
  • On April 8, 2022:
    • Electricity data from Ember was updated (using the Global Electricity Review 2022).
    • Data on greenhouse-gas emissions in electricity generation was added (greenhouse_gas_emissions).
    • Data on emissions intensity is now provided for most countries in the world.
  • On March 25, 2022:
    • Data on net electricity imports and electricity demand was added.
    • BP data was updated (using the Statistical Review of the World Energy 2021).
    • Maddison data on GDP was updated (using the Maddison Project Database 2020).
    • EIA data on primary energy consumption was included in the dataset.
    • Some issues in the dataset were corrected (for example some missing data in production by fossil fuels).
  • On February 14, 2022:
    • Some issues were corrected in the electricity data, and the energy dataset was updated accordingly.
    • The json and xlsx dataset files were removed from GitHub in favor of an external storage service, to keep this repository at a reasonable size.
    • The carbon_intensity_elec column was added back into the energy dataset.
  • On February 3, 2022, we updated the Ember global electricity data, combined with the European Electricity Review from Ember.
    • The carbon_intensity_elec column was removed from the energy dataset (since no updated data was available).
    • Columns for electricity from other renewable sources excluding bioenergy were added (namely other_renewables_elec_per_capita_exc_biofuel, and other_renewables_share_elec_exc_biofuel).
    • Certain countries and regions have been removed from the dataset, because we identified significant inconsistencies in the original data.
  • On March 31, 2021, we updated 2020 electricity mix data.
  • On September 9, 2020, the first version of this dataset was made available.

Data alterations

  • We standardize names of countries and regions. Since the names of countries and regions are different in different data sources, we harmonize all names to the Our World in Data standard entity names.
  • We create aggregate data for regions (e.g. Africa, Europe, etc.). Since regions are defined differently by our sources, we create our own aggregates following Our World in Data region definitions.
    • We also include data for regions as defined in the original datasets; for example, Europe (BP) corresponds to Europe as defined by BP.
  • We recalculate primary energy in terawatt-hours. The primary data sources on energy—the BP Statistical review of world energy, for example—typically report consumption in terms of exajoules. We have recalculated these figures as terawatt-hours using a conversion factor of 277.8.
  • We calculate per capita figures. All of our per capita figures are calculated from our population metric, which is included in the complete dataset.
    • We also calculate energy consumption per gdp, and include the corresponding gdp metric used in the calculation as part of the dataset.
  • We remove inconsistent data. Certain data points have been removed because their original data presented anomalies. They may be included again in further data releases if the anomalies are amended.

License

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license. You have the permission to use, distribute, and reproduce these in any medium, provided the source and authors are credited.

The data produced by third parties and made available by Our World in Data is subject to the license terms from the original third-party authors. We will always indicate the original source of the data in our database, and you should always check the license of any such third-party data before use.

Authors

This data has been collected, aggregated, and documented by Hannah Ritchie, Pablo Rosado, Edouard Mathieu, Max Roser.

Our World in Data makes data and research on the world’s largest problems understandable and accessible. Read more about our mission.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].