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 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 2019, renewable energy consumption accounted for around 13.8 percent of the world's energy consumption. Renewable energy is an important step in mitigating climate change and reducing the consequences caused by the phenomenon. Primary energy consumption from renewable sources
Primary energy is defined as a form of energy that can be encountered in nature and that has not undergone a transformation process. Both non-renewable and renewable sources can be considered forms of primary energy. Since 2006, the world’s usage of renewable energy has increased. In 2019, biofuels accounted for 9.4 percent of the world’s energy supply, while hydro energy made up about 2.5 percent. Renewable energies have become increasingly popular around the world as technologies like solar and wind become cheaper and more advanced.

China is leading in renewable energy installations China is currently one of the world’s leaders in renewable energy, consuming 7.8 exajoules from sustainable sources in 2020. The country’s growing renewable sector is surpassing both fossil fuels and nuclear power. China has become more reliant on renewable energy sources, as their establishment can be a foundation for energy security that is not subject to political instability. In 2020, China’s primary energy consumption reached over 82.3 exajoules for coal, in fact, the country accounts for nearly 23.6 percent of the world’s primary energy consumption. Currently, the Asia and Pacific region is considered the largest primary energy consumer by far, at 253 exajoules, followed by North America and Europe.

China is the largest consumer of primary energy in the world, using some 159.39 exajoules in 2022. This is far more than was consumed by the United States, which ranks second. The majority of primary energy fuels 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 of natural gas and renewable sources. Since 2009, the renewables share in total energy consumption has grown by around 16 percent. Overall, global primary energy consumption has increased over the last decade, but 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.

PK: Renewable Energy Consumption: % of Total Final Energy Consumption data was reported at 46.476 % in 2015. This records a decrease from the previous number of 46.605 % for 2014. PK: Renewable Energy Consumption: % of Total Final Energy Consumption data is updated yearly, averaging 49.972 % from Dec 1990 to 2015, with 26 observations. The data reached an all-time high of 58.091 % in 1991 and a record low of 44.276 % in 2007. PK: Renewable Energy Consumption: % of Total Final Energy Consumption data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Energy Production and Consumption. Renewable energy consumption is the share of renewables energy in total final energy consumption.; ; World Bank, Sustainable Energy for All (SE4ALL) database from the SE4ALL Global Tracking Framework led jointly by the World Bank, International Energy Agency, and the Energy Sector Management Assistance Program.; Weighted Average;

Increase Energy sales from $69.991 billion in 2013 to $78.775 billion by 2017.

Source: https://catalog.data.gov/dataset/energy-sales

This dataset featuring Consumption of Electrical Blower Machine with timeslots of around 10-15 min. this data is recorded with help of IoT device. Energy Consumption is Measured between Current and Previous time stamp. Null or less than 0.5 value of Energy Consumption means machine was of during respective time slot time . above time series consumption is Stationary with time as KWH i.e Kilo Watt/Hour Capacity of Blower motor is fixed.

TimeSeries #Stationary #HVAC #KWH

Over 10 years of hourly energy consumption data from PJM in Megawatts

Experimental data used to create regression models of appliances energy use in a low energy building.# Source: Luis Candanedo, luismiguel.candanedoibarra '@' umons.ac.be, University of Mons (UMONS).

Data Set Information:

The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters). For more information about the house, data collection, R scripts and figures, please refer to the paper and to the following github repository:

Attribute Information:

date time year-month-day hour:minute:second Appliances, energy use in Whlights, energy use of light fixtures in the house in WhT1, Temperature in kitchen area, in CelsiusRH_1, Humidity in kitchen area, in %T2, Temperature in living room area, in CelsiusRH_2, Humidity in living room area, in %T3, Temperature in laundry room areaRH_3, Humidity in laundry room area, in %T4, Temperature in office room, in CelsiusRH_4, Humidity in office room, in %T5, Temperature in bathroom, in CelsiusRH_5, Humidity in bathroom, in %T6, Temperature outside the building (north side), in CelsiusRH_6, Humidity outside the building (north side), in %T7, Temperature in ironing room , in CelsiusRH_7, Humidity in ironing room, in %T8, Temperature in teenager room 2, in CelsiusRH_8, Humidity in teenager room 2, in %T9, Temperature in parents room, in CelsiusRH_9, Humidity in parents room, in %To, Temperature outside (from Chievres weather station), in CelsiusPressure (from Chievres weather station), in mm HgRH_out, Humidity outside (from Chievres weather station), in %Wind speed (from Chievres weather station), in m/sVisibility (from Chievres weather station), in kmTdewpoint (from Chievres weather station), °Crv1, Random variable 1, nondimensionalrv2, Random variable 2, nondimensional Where indicated, hourly data (then interpolated) from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis, rp5.ru. Permission was obtained from Reliable Prognosis for the distribution of the 4.5 months of weather data.

Relevant Papers:

Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788, .

Citation Request:

Luis M. Candanedo, Veronique Feldheim, Dominique Deramaix, Data driven prediction models of energy use of appliances in a low-energy house, Energy and Buildings, Volume 140, 1 April 2017, Pages 81-97, ISSN 0378-7788, .

Source: http://archive.ics.uci.edu/ml/datasets/Appliances+energy+prediction

**History of renewable energy production in Occitanie, 2008-2021 **

The data in this dataset are either data directly from the data producers (RTE, SDES, ENEDIS, Observ’ER, ADEME, OibE, etc.) or data estimated on the basis of information collected from different data producers.

More information in the methodological note.

To access the analysis of these figures, the key figures for energy and greenhouse gases are available at the following link: https://www.arec-occitanie.fr/lobservatoire-services-aux-collectivites-et-publications.html

Current energy use and potential for renewable energies in Tuvalu. An Alofa Tuvalu Report, funded by the French Ministry for Foreign Affairs (Pacific Fund) and ADEME at the request of the Government of Tuvalu.

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Goal 7: Ensure access to affordable, reliable, sustainable and modern energy for all

Around the world there has been strong progress towards Goal 7. Electricity is becoming more renewable and more widely available. However, even though there has been a lot of progress, especially in less developed countries, there are still millions of people who lack access. The lack of electricity has become an even more pressing issue with the current pandemic, as many hospitals around the world are not able to adequately treat patients without medical equipment that relies on electricity. Beyond closing the gaps in access to electricity, more focus is needed on clean and safe cooking fuels and technologies, and expanding the use of renewable energy outside of the electricity sector. Here are some statistics from the U.N. regarding the status of Goal 7 around the globe.

• Global electrification rose from 83% in 2010 to 90% in 2018, but 789 million people still lacked access to electricity. Nearly 550 million of these people were in Sub-Saharan Africa
• Roughly 3 billion people still rely on wood, charcoal, and even animal waste for cooking and heating. Indoor air pollution, caused by combustible fuels caused 4.3 million deaths in 2012
• In 2017 only 17.3% of energy consumption came from renewable sources, up from 16.3% in 2010. Much faster progress is needed to reach long-term Climate goals.
• Currently, in some developing countries, as many as 1 in 4 hospitals are not electrified
• In 2017, financial flows to developing countries for renewable energy totaled more than 21 Billion, but only 12% went to LDC's (Least Developed Countries)

• For more statistics related to Affordable and Clean Energy, please see the Goal 7 Progress site here

  Goal 7 Targets:

• 7.1 By 2030, ensure universal access to affordable, reliable and modern energy services

• 7.2 By 2030, increase substantially the share of renewable energy in the global energy mix

• **7.3 **By 2030, double the global rate of improvement in energy efficiency

• 7.A By 2030, enhance international cooperation to facilitate access to clean energy research and technology, including renewable energy, energy efficiency and advanced and cleaner fossil-fuel technology, and promote investment in energy infrastructure and clean energy technology

• 7.B By 2030, expand infrastructure and upgrade technology for supplying modern and sustainable energy services for all in developing countries, in particular least developed countries, small island developing States, and land-locked developing countries, in accordance with their respective programmes of support

  This Month's Challenge:

  In August 2020, we are sharing more datasets thanks to our friends at the Centre for Humanitarian Data. Links to a number of datasets from the Humanitarian Data Exchange related to Affordable and Clean Energy can be found here. And as always, we are sharing Goal related data from the U.N.'s Sustainable Development Goal Database, with regional data on all of the targets listed above.

If you would like participate, you can use data from any source, but the overall goal is to focus your analysis on some of the energy related topics mentioned in the Targets above. If you come across other interesting datasets, please let us know so we can add them to this project The deadline for submission will be August 31, 2020. Make sure to tag us in your submission, add the #TheSDGVizProject hashtag, and add your submission to #TheSDGVizProject tracker.

Find energy data related to clean energy, energy markets, emissions, transportation and more.

This dataset contains information on energy consumption and various weather parameters such as solar radiation, temperature, pressure, humidity, wind speed, and precipitation. The "Energy delta[Wh]" column represents the change in energy consumption over a certain time period, while the "GHI" column measures the Global Horizontal Irradiance, which is the amount of solar radiation received by a horizontal surface. The dataset also includes information on the presence of sunlight ("isSun"), the length of daylight ("dayLength"), and the amount of time during which sunlight is available ("sunlightTime"). The "weather_type" column provides information on the overall weather conditions such as clear, cloudy, or rainy. The dataset is organized by hour and month, making it ideal for studying the relationship between renewable energy generation and weather patterns over time.

Energy Consumption: Fuel: NB: Electricity data was reported at 15,445,386.000 kWh th in Sep 2018. This records a decrease from the previous number of 16,230,832.000 kWh th for Aug 2018. Energy Consumption: Fuel: NB: Electricity data is updated monthly, averaging 16,603,146.000 kWh th from Jan 1999 to Sep 2018, with 237 observations. The data reached an all-time high of 19,041,353.000 kWh th in Jul 2008 and a record low of 13,102,955.000 kWh th in Feb 2009. Energy Consumption: Fuel: NB: Electricity data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.RB005: Energy Consumption.

Energy Consumption: Fuel: OB: Gasoline data was reported at 4,273.000 kl in May 2018. This records a decrease from the previous number of 4,742.000 kl for Apr 2018. Energy Consumption: Fuel: OB: Gasoline data is updated monthly, averaging 5,699.000 kl from Jan 1999 to May 2018, with 233 observations. The data reached an all-time high of 8,139.000 kl in Mar 2005 and a record low of 2,256.000 kl in Apr 2011. Energy Consumption: Fuel: OB: Gasoline data remains active status in CEIC and is reported by Ministry of Economy, Trade and Industry. The data is categorized under Global Database’s Japan – Table JP.RB004: Energy Consumption.

The dataset consists of energies and forces for monolayer graphene, bilayer graphene, graphite, and diamond in various states, including strained static structures and configurations drawn from ab initio MD trajectories. A total number of 4788 configurations was generated from DFT calculations using the Vienna Ab initio Simulation Package (VASP). The energies and forces are stored in the extended XYZ format. One file for each configuration.

Germany Energy Consumption: Other data was reported at 225.722 PJ in 2020. This records a decrease from the previous number of 225.974 PJ for 2019. Germany Energy Consumption: Other data is updated yearly, averaging 170.889 PJ from Dec 1990 to 2020, with 31 observations. The data reached an all-time high of 267.189 PJ in 2011 and a record low of 11.819 PJ in 1999. Germany Energy Consumption: Other data remains active status in CEIC and is reported by Federal Ministry for Economic Affairs and Climate Action. The data is categorized under Global Database’s Germany – Table DE.RB003: Energy Consumption.

Machine-learning potentials are usually trained on the ground-state, Born-Oppenheimer energy surface, which depends exclusively on the atomic positions and not on the simulation temperature. This disregards the effect of thermally excited electrons, that is important in metals, and essential to the description of warm dense matter. An accurate physical description of these effects requires that the nuclei move on a temperature-dependent electronic free energy. We propose a method to obtain machine-learning predictions of this free energy at an arbitrary electron temperature using exclusively training data from ground-state calculations, avoiding the need to train temperature-dependent potentials, and benchmark it on metallic liquid hydrogen at the conditions of the core of gas giants and brown dwarfs. This Letter demonstrates the advantages of hybrid schemes that use physical consideration to combine machine-learning predictions, providing a blueprint for the development of similar approaches that extend the reach of atomistic modeling by removing the barrier between physics and data-driven methodologies. This record contains the raw outputs of the DFT calculations done on the training set. These files are denoted by the "training set folder #*" description. The record also contains a minimal working example showing the ML workflow for training the data and how to run the MD simulations. We include the training data in the format of XYZ files for the structures and NumPy arrays for the DFT energies, forces and DOS. We also provide a Chemiscope visualisation file of the training set.

Since 2012, the State Office for Nature, Environment and Consumer Protection NRW (LANUV NRW) has been conducting potential studies on renewable and climate-friendly energies in NRW. The following energies were considered: Bioenergy, geothermal energy, industrial waste heat, cogeneration, photovoltaic roof, photovoltaic open space, pumped storage power, solar thermal, warm pit water, hydropower and wind energy. The results are published in various specialist reports at www.lanuv.nrw.de and are presented in the specialist information system Energieatlas NRW (www.energieatlas.nrw.de). The Excel table summarises the results of all studies.