The East of England accounted for the highest average household electricity consumption in Great Britain in 2022. That year, it added up to 3,720 kilowatt-hours per household. In comparison, households in the North East of England had the lowest average consumption, at 2,906 kilowatt-hours per household. End user consumption of electricity in the UK Despite continual annual declines in consumption since 2005, households have remained the largest electricity end-users in the UK since 2005. In 2020, amidst lockdown measures following the coronavirus outbreak, the gap between domestic and industrial consumption grew even wider. In 2022, domestic electricity consumption in the UK amounted to 96 terawatt-hours, while industrial consumption recorded the lowest figure of the century, at some 85 terawatt-hours. Declining electricity consumption in the UK Electricity consumption in the UK has seen a dramatic decrease in the past two decades, at least partially related to increased efficiency in equipment and distribution. In 2022, consumption from all electricity suppliers in the UK stood at 275 terawatt-hours. Projections indicate that the UK's electricity consumption will resume a growing trend after 2025, to surpass 32 million tons of oil equivalent in 2040.

This table shows regional figures on the average consumption of energy (natural gas and electricity) of private dwellings broken down by type of dwelling and ownership for Nederland, group of provinces, provinces and municipalities. Besides, for total dwellings only, the share of heat distribution (district heating) has been added, because this is relevant for the interpretation of the height of the average consumption of natural gas.

Data available from: 2010

Status of the figures: All figures from 2010 - 2021 are definite. Figures of 2022 and 2023 are revised provisional. Figures for 2024 are provisional.

Changes as of September 2025: Figures added for 2024. Figures for 2022 and 2023 have been revised based on smart-meter data. These figures are more accurate than figures based on standard yearly consumption data.

Changes as of October 2023: Provisional figures of 2022 have been added. Figures of 2021 have been updated. The category “Average consumption of electricity” is replaced by “Average supply of electricity” and a category “Average net supply of electricity” has been added.

When will new figures be published? A revision to the method of this statistic is currently underway, causing the table to be delayed. New figures will come in the 3rd quarter of the folowing year.

Iran Electricity Consumption: Household data was reported at 18,339.000 kWh mn in Jun 2018. This records an increase from the previous number of 16,260.000 kWh mn for Mar 2018. Iran Electricity Consumption: Household data is updated quarterly, averaging 15,589.000 kWh mn from Jun 2008 (Median) to Jun 2018, with 41 observations. The data reached an all-time high of 28,016.100 kWh mn in Sep 2017 and a record low of 12,262.000 kWh mn in Mar 2012. Iran Electricity Consumption: Household data remains active status in CEIC and is reported by Ministry of Energy. The data is categorized under Global Database’s Iran – Table IR.RB002: Electricity Generation and Consumption.

In 2023, German five-person households in detached houses with electric water heating consumed around ***** kilowatt hours of electricity a year, on average. Apartment buildings with the same system consumed ***** kilowatt hours.

The Nordic countries of Iceland, Norway, Finland, and Sweden recorded the largest electricity demand per capita in Europe over the last few years. In 2023, Iceland’s per capita electricity demand averaged at almost 52 megawatt-hours per person, more than twice the consumption of runner-up Norway. Meanwhile, France, the largest electricity producer among the European Union countries, had an average per capita consumption of seven megawatt-hours in 2024. Why is electricity consumption so high in Iceland? The intense electricity consumption in Iceland stems from a combination of factors. On the one hand, due to its abundant natural resources, hydro and geothermal are the main sources of electricity generation in Iceland, allowing the country to produce power at high reliability and very low costs. Meanwhile, on the demand side, Iceland is home to some particularly energy-intensive industries – in 2023, it ranked among the largest aluminum smelter producers worldwide. To top it off, electricity demand is also high on the household sector side, due to the Nordic country’s long, dark, and cold winters. A similar combination of factors can also be found in Norway. How cheap is electricity in Iceland? In 2024, household electricity prices in Iceland averaged between 18 and 22.2 euro cents per kilowatt-hour, depending on the level of demand. In comparison to the average household electricity price in the European Union in the same period, it was around 40 percent cheaper. Iceland had one of the lowest residential electricity prices in Europe. On the other side of the spectrum, Germany, Denmark, and Belgium reported the highest prices in the region.

This table contains 165 series, with data for years 2011-2019 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia; ...) ; Energy type (4 items: Total, all energy types; Electricity; Natural gas; Heating oil) ; Energy consumption (4 items: Gigajoules; Gigajoules per household; Proportion of total energy; Number of households).

China Electricity Consumption: per Capita: Average data was reported at 6,257.000 kWh in 2022. This records an increase from the previous number of 6,032.000 kWh for 2021. China Electricity Consumption: per Capita: Average data is updated yearly, averaging 1,066.997 kWh from Dec 1978 (Median) to 2022, with 45 observations. The data reached an all-time high of 6,257.000 kWh in 2022 and a record low of 261.265 kWh in 1978. China Electricity Consumption: per Capita: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCB: Electricity Summary.

Iceland is by far the largest per capita consumer of electricity worldwide, averaging 51.9 megawatt-hours per person in 2024. This results from a combination of factors, such as low-cost electricity production, increased heating demand, and the presence of energy-intensive industries in the country. Norway, Qatar, and Canada were also some of the world's largest electricity consumers per capita that year. China is the leading overall power consumer Power-intensive industries, the purchasing power of the average citizen, household size, and general power efficiency standards all contribute to the amount of electricity that is consumed per person every year. However, in terms of total electricity consumption, a country's size and population can also play an important role. In 2024, the three most populous countries in the world, namely China, the United States, and India, were also the three largest electricity consumers. Global electricity consumption on the rise In 2023, net electricity consumption worldwide amounted to over 27,000 terawatt-hours, an increase of 30 percent in comparison to a decade earlier. When compared to 1980, global electricity consumption more than tripled. On the generation side, the world is still strongly dependent on fossil fuels. Despite the world's renewable energy capacity quintupling in the last decade, coal and gas combined still accounted for almost 60 percent of global electricity generation in 2023.

🏠 TNEB Household Power Consumption (Before Solar Installation) 🔋 Tamil Nadu Home Electricity Usage – Real Data before Rooftop Solar Setup 📌 Dataset Description This dataset contains monthly power consumption data for a residential customer under Tamil Nadu Electricity Board (TNEB) prior to the installation of a rooftop solar system.

The objective is to help analyze energy usage patterns in Indian households, estimate the potential benefits of solar adoption, and support machine learning models related to energy forecasting and savings calculation.

📂 What’s Included Column Name Description Assessment Date Date of consumption record Entry Date Date of entry into system Status Meter status – NORMAL or NOT IN USE KWH Energy consumption in kilowatt-hours Recorded Demand (kW) Maximum recorded demand Power Factor Average power factor CC Charges Consumption charges Electricity Tax Applicable tax Fixed Charges Monthly fixed charges Total Charges Total calculated charges Advance Paid Any advance amount paid Adjustment Adjustment entries Amount To Be Paid Final bill amount after deductions Due Date Last date for payment Amount Paid Paid amount Receipt No Transaction receipt number Payment Date Date of payment

🧾 File Information Filename: TNEB_Power_Consumption_Before_Solar.csv

Rows: 25+

Time Period: June 2021 – April 2025

Data Format: CSV (Comma-Separated Values)

Size: ~20 KB

🖼️ Images Include these images in your dataset (upload to Kaggle interface):

📊 Monthly Power Consumption Chart – bar graph of units consumed month-wise.

💸 Bill Amount Trend – line chart showing monthly bill values.

☀️ Before vs After Solar Comparison (optional) – if you plan to upload the after-solar dataset.

✅ Use Cases Energy load pattern analysis

Solar feasibility estimation

Consumption forecasting (ML models)

Real-world use for electrical savings simulation

📌 Future Scope A follow-up dataset for post-solar installation data can be added to help compare savings and performance improvement.

As of 2023, the electricity consumption per capita in Indonesia amounted to around ***** kilowatt hours. In the past few years, the government has been working to steadily increase the electrification rate in Indonesia.

Kenya Electricity Consumption: Annual: Domestic and Small Commercial data was reported at 3,528.300 kWh mn in 2017. This records an increase from the previous number of 3,315.700 kWh mn for 2016. Kenya Electricity Consumption: Annual: Domestic and Small Commercial data is updated yearly, averaging 1,076.250 kWh mn from Dec 1976 (Median) to 2017, with 42 observations. The data reached an all-time high of 3,528.300 kWh mn in 2017 and a record low of 302.000 kWh mn in 1976. Kenya Electricity Consumption: Annual: Domestic and Small Commercial data remains active status in CEIC and is reported by Kenya National Bureau of Statistics. The data is categorized under Global Database’s Kenya – Table KE.RB002: Electricity Consumption.

Thailand Electricity Consumption: Northern: Residential data was reported at 488.800 kWh mn in Aug 2018. This records a decrease from the previous number of 511.600 kWh mn for Jul 2018. Thailand Electricity Consumption: Northern: Residential data is updated monthly, averaging 279.700 kWh mn from Jan 1995 (Median) to Aug 2018, with 284 observations. The data reached an all-time high of 621.000 kWh mn in May 2016 and a record low of 132.000 kWh mn in Feb 1995. Thailand Electricity Consumption: Northern: Residential data remains active status in CEIC and is reported by Bank of Thailand. The data is categorized under Global Database’s Thailand – Table TH.RB003: Electricity Consumption: Regional Statistics.

240,000+ Household Electricity Consumption Records for Machine Learning

A Dataset for The Smart Way to Save Energy

About this dataset

Looking to better understand how energy is used in the home? This dataset can help. It contains six months of electricity consumption data for a household, gathered between January 2007 and June 2007. The data includes information on global active power, global reactive power, voltage, global intensity, sub-metering 1 (kitchen), sub-metering 2 (laundry room), and sub-metering 3 (electric water heater and air conditioner). With 260,640 measurements in total, this dataset can provide crucial insights into understanding household electricity consumption

How to use the dataset

This dataset can be used for machine learning purposes such as predictive modeling or time series analysis. For example, one could use this dataset to predict future household electricity consumption based on past data.

Some possible research ideas include: 1. Analyzing the effects of different types of electrical devices on power consumption 2. Studying how power consumption changes over time and by location 3. Constructing a predictive model to forecast future power consumption

Research Ideas

1. Analyze the effects of different types of electrical devices on power consumption
2. Study how power consumption changes over time and by location
3. Construct a predictive model to forecast future power consumption

Acknowledgements

We would like to thank the databeats team for providing this dataset. If you use this dataset in your research, please credit the original authors: Georges Hébrail and Alice Bérard

License

Unknown License - Please check the dataset description for more information.

Columns

File: household_power_consumption.csv | Column name | Description | |:--------------------------|:--------------------------------------------------------------------------------------------------| | Date | The date of the observation. (Date) | | Time | The time of the observation. (Time) | | Global_active_power | The total active power consumed by the household (kilowatts). (Numeric) | | Global_reactive_power | The total reactive power consumed by the household (kilowatts). (Numeric) | | Voltage | The voltage at which the electricity is delivered to the household (volts). (Numeric) | | Global_intensity | The average current intensity delivered to the household (amps). (Numeric) | | Sub_metering_1 | The active power consumed by the kitchen (kilowatts). (Numeric) | | Sub_metering_2 | The active power consumed by the laundry room (kilowatts). (Numeric) | | Sub_metering_3 | The active power consumed by the electric water heater and air conditioner (kilowatts). (Numeric) |

🏡 Household Energy Consumption - April 2025 (90,000 Records)

📌 Overview

This dataset presents detailed energy consumption records from various households over the month. With 90,000 rows and multiple features such as temperature, household size, air conditioning usage, and peak hour consumption, this dataset is perfect for performing time-series analysis, machine learning, and sustainability research.

📂 Dataset Summary

• Rows: 90,000
• Time Range: April 1, 2025 – April 30, 2025
• Data Granularity: Daily per household
• Location: Simulated global coverage
• Format: CSV (Comma-Separated Values)

📚 Libraries Used for Working with household_energy_consumption_2025.csv

🔍 1. Data Manipulation & Analysis

📊 2. Data Visualization

📈 3. Machine Learning / Modeling

🧹 4. Data Preprocessing

🧪 5. Model Evaluation

✅ These libraries provide a complete toolkit for performing data analysis, modeling, and visualization tasks efficiently.

📈 Potential Use Cases

This dataset is ideal for a wide variety of analytics and machine learning projects:

🔮 Forecasting & Time Series Analysis

• Predict future household energy consumption based on previous trends and weather conditions.
• Identify seasonal and daily consumption patterns.

💡 Energy Efficiency Analysis

• Analyze differences in energy consumption between households with and without air conditioning.
• Compare energy usage efficiency across varying household sizes.

🌡️ Climate Impact Studies

• Investigate how temperature affects electricity usage across households.
• Model the potential impact of climate change on residential energy demand.

🔌 Peak Load Management

• Build models to predict and manage energy demand during peak hours.
• Support research on smart grid technologies and dynamic pricing.

🧠 Machine Learning Projects

• Supervised learning (regression/classification) to predict energy consumption.
• Clustering households by usage patterns for targeted energy programs.
• Anomaly detection in energy usage for fault detection.

🛠️ Example Starter Projects

• Time-series forecasting using Facebook Prophet or ARIMA
• Regression modeling using XGBoost or LightGBM
• Classification of AC vs. non-AC household behavior
• Energy-saving recommendation systems
• Heatmaps of temperature vs. energy usage

The share of electricity consumption for households in Czechia has slightly shifted to consumption bands equal to or higher than 15,000 kWh from 2017 to 2024. The highest share of household electricity consumption in 2024 was consumption bands between 5,000 kWh and 14,999 kWh at 37 percent.

Egypt Electricity Consumption: Commercial and Domestic: Household data was reported at 6,446.000 kWh mn in Jun 2018. This records an increase from the previous number of 5,609.000 kWh mn for May 2018. Egypt Electricity Consumption: Commercial and Domestic: Household data is updated monthly, averaging 3,859.000 kWh mn from Jul 2001 (Median) to Jun 2018, with 204 observations. The data reached an all-time high of 7,644.000 kWh mn in Jun 2016 and a record low of 2,032.000 kWh mn in Apr 2002. Egypt Electricity Consumption: Commercial and Domestic: Household data remains active status in CEIC and is reported by Ministry of Electricity and Energy. The data is categorized under Global Database’s Egypt – Table EG.RB004: Electricity Consumption.

Electricity consumption in the United States totaled ***** terawatt-hours in 2024, the highest value in the period under consideration. Figures represent energy end use, which is the sum of retail sales and direct use of electricity by the producing entity. Electricity consumption in the U.S. is expected to continue increasing in the coming years. Which sectors consume the most electricity in the U.S.? Consumption has often been associated with economic growth. Nevertheless, technological improvements in efficiency and new appliance standards have led to a stabilizing of electricity consumption, despite the increased ubiquity of chargeable consumer electronics. Electricity consumption is highest in the residential sector, followed by the commercial sector. Equipment used for space heating and cooling account for some of the largest shares of residential electricity end use. Leading states in electricity use Industrial hub Texas is the leading electricity-consuming U.S. state. In 2023, the southwestern state, which houses major refinery complexes and is also home to over ** million people, consumed almost ****terawatt-hours. Florida and California followed in second and third, with an annual consumption of approximately *** terawatt-hours and 240 terawatt-hours, respectively.

Azerbaijan Electricity Consumption: Households data was reported at 6,625.800 kWh mn in 2023. This records an increase from the previous number of 6,569.100 kWh mn for 2022. Azerbaijan Electricity Consumption: Households data is updated yearly, averaging 7,134.600 kWh mn from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 13,795.000 kWh mn in 2006 and a record low of 4,075.000 kWh mn in 1995. Azerbaijan Electricity Consumption: Households data remains active status in CEIC and is reported by The State Statistical Committee of the Republic of Azerbaijan. The data is categorized under Global Database’s Azerbaijan – Table AZ.RB006: Electricity Consumption.

Abstract

PCBS implemented the household energy survey (July 2008). This survey collected data on household energy indicators (electricity, petroleum fuel) in the household activities (cooking, backing, water heating, lighting, and conditioning). Data collection took place during the period 24/08/2008 - 02/10/2008. The results of the survey indicated that 99.8% of households in the Palestinian Territory were connected to the public electricity network in July 2008, while this percentage was 99.5% in July 2006. From the results, it is noted that the South of West Bank has the lowest percentage of households connected to electricity network 98.9%. It is noted that 81.6% of households in the West Bank used normal Electricity Meter. 18.4% of households used pre-paid Electricity Meter, this percentage was about 36.1% in the North of West Bank, 7.1%in the South of West Bank, and 5.2% in the Middle of West Bank during July 2008. The survey showed that 68.2% of households in the Palestinian Territory utilized solar energy by using solar energy heaters in July 2008, whereas this percentage was 69.2% in July 2006. It is noted that this percentage differs within the Palestinian regions in July 2008; it was about 71.9% in Gaza Strip and 66.3% in the West Bank. The results of the survey indicated that the average household electricity consumption (from the households that used electricity) in the Palestinian Territory during July 2008 was 271 KWh, where this average was 227 KWh during July 2006. This average varied by region and type of locality in July 2008: it reached 301 KWh in the Middle of West Bank and did not exceed 229 KWh in the North of West Bank. The average was about 282 KWh in urban localities and 226 KWh in rural localities, and 296 KWh in refugee camps. The average per capita electricity consumption in the Palestinian Territory during July 2008 was 46.7 KWh. The main findings of the survey indicate that the average household gasoline consumption in the Palestinian Territory during July 2008 was 9 liters. This average ranged by type of locality and region, it reached 12 liters in urban localities, 7 liters in rural localities and 3 liters in refugee camps. This average was about 25 liters in the Middle of West Bank and didn’t exceed 8 liters in the North of West Bank, and 1 liter in Gaza Strip. The survey indicated that 88.7% of households in the Palestinian Territory used liquefied petroleum gas; the average household liquefied petroleum gas consumption was 14 kg (from the households that used liquefied petroleum gas), while it was 17 kg in July 2006. This average varies by region and type of locality in July 2008; it reached 17 kg in the South of West Bank, and did not exceed 11 kg in Gaza Strip. 79.7% of households used conditioning facilities in July 2008, distributed as 78.7% in the West Bank, and 81.9% in Gaza Strip. The main results of the survey indicate that 5.4% of households in the Palestinian Territory have used electrical conditioner, 45.4% of the households used fixed fan, and 75.5% of the households used mobile fan in July 2008.

Geographic coverage

according to area (West Bank(North, Middle, South), Gaza Strip)

Analysis unit

Households

Universe

All Palestinian Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Two stage Stratified Cluster Random sample The Frame is the Sencus 1997 Sub Sample of the labour force Survey

Sampling deviation

No Deviations

Mode of data collection

Face-to-face [f2f]

Research instrument

This section presents a documentation of the methodology used in preparing this survey.

The household energy survey questionnaire was designed in accordance with similar country experience and with international standards and recommendations for the most important indicators, taking into account the special situation of the Palestinian Territory.

Cleaning operations

Editing and coding before data entry: All questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

SPSS files where checked after data entry according to the instructions.

Response rate

During fieldwork 2,773 Households were visited in the Palestinian Territory, the end results for the interview become as following: (2,456) complete questioner (32) traveling households (21) housing unit not existed (55) cases no body in the house (33) objection cases (139) housing unit abandoned (14) household can't give data (23) other cases

Sampling error estimates

These types of errors evolved as a result of studying a part of the society and not all of it, and because this survey is a sample, the data of this survey will be affected by sampling errors due to using a sample and not the whole frame of the society, and so differences appear compared with the actual values that could be obtained through a census. For this survey, variance calculations were made for average household consumption and total consumption for the different types of energy in the Palestinian Territory.

The results of wood, charcoal and olive cake suffers from a high variance. This problem should be taken into consideration when dealing with the average household consumption of these types of fuel, keeping in mind that there is no problem in publishing the data among the geographical level (North of the West Bank, Middle of the West Bank, South of the West Bank and Gaza Strip). However, publishing data for the governorate level is not possible due to the high variance especially for wood, charcoal and olive cake. The variances for the main indicators of this survey are as follows

95% Confidence Interval C.V % Standard Error Estimate Variable
Upper Lower Value Unit
99.9 99.5 0.001 0.1 99.8 % Main Electricity Source 71.4 64.9 0.024 1.6 68.2 % Use of Solar Heaters
280 261 0.018 4.85 271 KWh Average Electricity Consumption 149 121 0.052 7.02 135 Kg Average wood Consumption
11.2 7.6 0.096 0.9 9 Liter Average Gasoline Consumption

Data appraisal

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