Over the past half a century, the world's electricity consumption has continuously grown, reaching approximately 27,000 terawatt-hours by 2024. Between 1980 and 2024, electricity consumption more than tripled, while the global population reached eight billion people. Growth in industrialization and electricity access across the globe has further boosted electricity demand. China's economic rise and growth in global power use Since 2000, China's GDP has recorded an astonishing 15-fold increase, turning it into the second-largest global economy, behind only the United States. To fuel the development of its billion-strong population and various manufacturing industries, China requires more energy than any other country. As a result, it has become the largest electricity consumer in the world. Electricity consumption per capita In terms of per capita electricity consumption, China and other BRIC countries are still vastly outpaced by developed economies with smaller population sizes. Iceland, with a population of less than half a million inhabitants, consumes by far the most electricity per person in the world. Norway, Qatar, Canada, and the United States also have among the highest consumption rates. Multiple contributing factors, such as the existence of power-intensive industries, household sizes, living situations, appliance and efficiency standards, and access to alternative heating fuels, determine the amount of electricity the average person requires in each country.

In 2023, it was estimated that artificial intelligence represented a power consumption of *** gigawatts worldwide, or eight percent of total power consumption in data centers that year. AI's power consumption is expected to grow significantly in the next five years, forecast at ** to **** gigawatts in 2028. That would account for up to ** percent of data center power consumption that year.

How much electricity does an American household use each month?

This data set provides monthly electricity used by households, averaged for each US state for the year 2011.

Electricity use is measured in kilowatts per hour, or kWh. One 100-watt light bulb burning for ten hours is equal to one kWh of electricity.

How do northern and southern states compare in average momthly electricity consumption? How does the price of electricity compare in different US regions?

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.

The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset provides node-level energy consumption data collected via watt-meters, complementing the primary BUTTER dataset. This dataset records energy consumption and performance metrics for 1,059,206 experimental runs across diverse configurations of fully connected neural networks. Key attributes include:

1.timestamp: The precise time of the energy consumption measurement. 2.node:The hardware node identifier (e.g., r103u05) where the experiment was conducted. 3.watts: The energy consumption (in watts) recorded for the corresponding node at the given timestamp.

Highlights Data spans 30,582 distinct configurations, including variations across 13 datasets, 20 network sizes, 8 network shapes, and 14 depths. Measurements were taken on CPU and GPU hardware, offering insights into the relationship between neural network parameters and energy consumption. The dataset provides valuable information for analyzing the energy efficiency of deep learning models, particularly in relation to cache effects, dataset size, and network architecture.

Use Cases This dataset is ideal for: Energy-efficient AI research: Understanding how energy consumption scales with model size, dataset properties, and network configurations. Performance optimization: Identifying configurations with optimal trade-offs between performance and energy usage. Sustainability analysis: Evaluating the carbon footprint of training and deploying deep learning models.

Saudi Arabia SA: Electric Power Consumption: per Capita data was reported at 9,444.216 kWh in 2014. This records an increase from the previous number of 8,816.317 kWh for 2013. Saudi Arabia SA: Electric Power Consumption: per Capita data is updated yearly, averaging 4,546.315 kWh from Dec 1971 (Median) to 2014, with 44 observations. The data reached an all-time high of 9,444.216 kWh in 2014 and a record low of 323.718 kWh in 1971. Saudi Arabia SA: Electric Power Consumption: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank: Energy Production and Consumption. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

The indicator measures how much electricity and heat every citizen consumes at home excluding energy used for transportation. Since the indicator refers to final energy consumption, only energy used by end consumers is considered. The related consumption of the energy sector itself is excluded.

Morocco MA: Electric Power Consumption: per Capita data was reported at 901.128 kWh in 2014. This records an increase from the previous number of 878.587 kWh for 2013. Morocco MA: Electric Power Consumption: per Capita data is updated yearly, averaging 400.854 kWh from Dec 1971 (Median) to 2014, with 44 observations. The data reached an all-time high of 901.128 kWh in 2014 and a record low of 125.220 kWh in 1971. Morocco MA: Electric Power Consumption: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Morocco – Table MA.World Bank: Energy Production and Consumption. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

Algeria DZ: Electric Power Consumption: per Capita data was reported at 1,827.953 kWh in 2023. This records an increase from the previous number of 1,756.587 kWh for 2022. Algeria DZ: Electric Power Consumption: per Capita data is updated yearly, averaging 862.878 kWh from Dec 1990 (Median) to 2023, with 34 observations. The data reached an all-time high of 1,827.953 kWh in 2023 and a record low of 539.608 kWh in 1990. Algeria DZ: Electric Power Consumption: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Algeria – Table DZ.World Bank.WDI: Environmental: Energy Production and Consumption. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.;IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25;Weighted average;

Source:

Da Silva, D. G.; Geller M. T. B., Moura M. S. S., Meneses, A. A. M., 2022. Performance Evaluation of LSTM Neural Networks for Consumption Prediction. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 2, 100030. https://doi.org/10.1016/j.prime.2022.100030.

Data Set Information:

The LABIC-Building data set is related to a building of the Federal University of Western Pará (Universidade Federal do Oeste do Pará, UFOPA - Coordinated by Professor DSc Anderson Alvarenga De Moura Meneses) with high power demand of AC systems, which is a characteristic of the Amazon region.

Data were obtained is Santarém city, Pará State, Brazil (Da Silva et al., 2021, 2022).

The time series contains 256.092 points of aggregated active power in Watts (W). After downsampling the data in 10 minutes intervals, the data set remained with 33.830 points.

Attribute Information: Date: Date YYYY/MM/DD and Time: HH:MM:SS Active Power: active power in Watts (W)

Citation Request: If you use this dataset in your research, please cite the following paper: Da Silva, D. G.; Geller M. T. B., Moura M. S. S., Meneses, A. A. M., 2022. Performance Evaluation of LSTM Neural Networks for Consumption Prediction. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 2, 100030. https://doi.org/10.1016/j.prime.2022.100030.

Relevant Papers: Da Silva, D. G.; Geller M. T. B., Moura M. S. S., Meneses, A. A. M., 2022. Performance Evaluation of LSTM Neural Networks for Consumption Prediction. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 2, 100030. https://doi.org/10.1016/j.prime.2022.100030.

Da Silva, D. G.; Geller M. T. B., Moura M. S. S., Meneses, A. A. M., 2021. A Deep Learning Prediction Module for the IoT system EnergySaver for Monitoring and Estimating Power Consumption. In 16th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES), Dubrovnik, Croácia.

DA SILVA, D. G.; TEIXEIRA, Y. B.; VARÃO, D. F. S.; SANTOS, C. A. M.; MOURA, M. S. S.; GELLER, M. T. B.; BENTES, J; MENESES, A. A. M. EnergySaver Software Manual. arXiv preprint arXiv:2107.06664, 2021. Disponível em:

Energy consumption of artificial intelligence (AI) models in training is considerable, with both GPT-3, the original release of the current iteration of OpenAI's popular ChatGPT, and Gopher consuming well over **********-megawatt hours of energy simply for training. As this is only for the training model it is likely that the energy consumption for the entire usage and lifetime of GPT-3 and other large language models (LLMs) is significantly higher. The largest consumer of energy, GPT-3, consumed roughly the equivalent of *** Germans in 2022. While not a staggering amount, it is a considerable use of energy. Energy savings through AI While it is undoubtedly true that training LLMs takes a considerable amount of energy, the energy savings are also likely to be substantial. Any AI model that improves processes by minute numbers might save hours on shipment, liters of fuel, or dozens of computations. Each one of these uses energy as well and the sum of energy saved through a LLM might vastly outperform its energy cost. A good example is mobile phone operators, of which a ***** expect that AI might reduce power consumption by *** to ******* percent. Considering that much of the world uses mobile phones this would be a considerable energy saver. Emissions are considerable The amount of CO2 emissions from training LLMs is also considerable, with GPT-3 producing nearly *** tonnes of CO2. This again could be radically changed based on the types of energy production creating the emissions. Most data center operators for instance would prefer to have nuclear energy play a key role, a significantly low-emission energy producer.

El Salvador SV: Electric Power Consumption: per Capita data was reported at 939.153 kWh in 2014. This records a decrease from the previous number of 961.320 kWh for 2013. El Salvador SV: Electric Power Consumption: per Capita data is updated yearly, averaging 412.536 kWh from Dec 1971 (Median) to 2014, with 44 observations. The data reached an all-time high of 961.320 kWh in 2013 and a record low of 169.714 kWh in 1971. El Salvador SV: Electric Power Consumption: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s El Salvador – Table SV.World Bank: Energy Production and Consumption. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

Colombia Primary Energy Consumption per Capita data was reported at 11,760.556 kWh/Person in 2022. This records an increase from the previous number of 11,156.515 kWh/Person for 2021. Colombia Primary Energy Consumption per Capita data is updated yearly, averaging 7,982.834 kWh/Person from Dec 1965 (Median) to 2022, with 58 observations. The data reached an all-time high of 11,760.556 kWh/Person in 2022 and a record low of 5,344.405 kWh/Person in 1965. Colombia Primary Energy Consumption per Capita data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s Colombia – Table CO.OWID.ESG: Environmental: CO2 and Greenhouse Gas Emissions: Annual.

DK: Electricity Production From Hydroelectric Sources: % of Total data was reported at 0.063 % in 2015. This records an increase from the previous number of 0.047 % for 2014. DK: Electricity Production From Hydroelectric Sources: % of Total data is updated yearly, averaging 0.100 % from Dec 1960 (Median) to 2015, with 56 observations. The data reached an all-time high of 0.489 % in 1961 and a record low of 0.035 % in 1996. DK: Electricity Production From Hydroelectric Sources: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank: Energy Production and Consumption. Sources of electricity refer to the inputs used to generate electricity. Hydropower refers to electricity produced by hydroelectric power plants.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown. Restricted use: Please contact the International Energy Agency for third-party use of these data.

DK: Electricity Production From Nuclear Sources: % of Total data was reported at 0.000 % in 2015. This stayed constant from the previous number of 0.000 % for 2014. DK: Electricity Production From Nuclear Sources: % of Total data is updated yearly, averaging 0.000 % from Dec 1960 (Median) to 2015, with 56 observations. DK: Electricity Production From Nuclear Sources: % of Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank: Energy Production and Consumption. Sources of electricity refer to the inputs used to generate electricity. Nuclear power refers to electricity produced by nuclear power plants.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Electricity production shares may not sum to 100 percent because other sources of generated electricity (such as geothermal, solar, and wind) are not shown. Restricted use: Please contact the International Energy Agency for third-party use of these data.

Botswana BW: Electric Power Consumption: per Capita data was reported at 1,522.199 kWh in 2022. This records an increase from the previous number of 1,455.793 kWh for 2021. Botswana BW: Electric Power Consumption: per Capita data is updated yearly, averaging 1,463.311 kWh from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 1,773.725 kWh in 2015 and a record low of 758.216 kWh in 1990. Botswana BW: Electric Power Consumption: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Botswana – Table BW.World Bank.WDI: Environmental: Energy Production and Consumption. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.;IEA Energy Statistics Data Browser, International Energy Agency (IEA), uri: https://www.iea.org/data-and-statistics/data-tools/energy-statistics-data-browser, publisher: International Energy Agency (IEA), date accessed: 2025-03-25;Weighted average;

DK: Alternative and Nuclear Energy: % of Total Energy Use data was reported at 8.172 % in 2015. This records an increase from the previous number of 7.468 % for 2014. DK: Alternative and Nuclear Energy: % of Total Energy Use data is updated yearly, averaging 0.127 % from Dec 1960 (Median) to 2015, with 56 observations. The data reached an all-time high of 8.172 % in 2015 and a record low of 0.010 % in 1977. DK: Alternative and Nuclear Energy: % of Total Energy Use data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank: Energy Production and Consumption. Clean energy is noncarbohydrate energy that does not produce carbon dioxide when generated. It includes hydropower and nuclear, geothermal, and solar power, among others.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted average; Restricted use: Please contact the International Energy Agency for third-party use of these data.

Philippines PH: Electric Power Consumption: per Capita data was reported at 699.205 kWh in 2014. This records an increase from the previous number of 685.665 kWh for 2013. Philippines PH: Electric Power Consumption: per Capita data is updated yearly, averaging 367.979 kWh from Dec 1971 (Median) to 2014, with 44 observations. The data reached an all-time high of 699.205 kWh in 2014 and a record low of 235.760 kWh in 1971. Philippines PH: Electric Power Consumption: per Capita data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Philippines – Table PH.World Bank: Energy Production and Consumption. Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution, and transformation losses and own use by heat and power plants.; ; IEA Statistics © OECD/IEA 2014 (http://www.iea.org/stats/index.asp), subject to https://www.iea.org/t&c/termsandconditions/; Weighted Average; Restricted use: Please contact the International Energy Agency for third-party use of these data.