Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

The "Indian Household Electricity Consumption Dataset" is a comprehensive dataset that captures the electricity consumption patterns of Indian households. It includes information about the number of hours each household appliance, such as fans, air conditioners, refrigerators, televisions, monitors, and motor pumps, operates in a month. Additionally, it incorporates demographic factors like the city of residence and the electricity distribution company serving that city.

This dataset is designed to help predict the monthly electricity bills for Indian households based on appliance usage and other relevant factors. It can be used in various applications, such as - Energy management and Consumer insights.

Summary

This dataset provides a detailed look into energy consumption across various household appliances within smart homes. The dataset includes 100,000 records, each tracking energy usage, appliance type, time of consumption, and associated environmental factors such as outdoor temperature and season. Additionally, it includes information on household size, allowing for a deeper exploration of energy consumption patterns by family size and weather conditions. This dataset is ideal for energy usage forecasting, sustainability studies, and machine learning projects aimed at optimizing household energy efficiency.

• Home ID: A unique identifier for each home (anonymized).
• Appliance Type: The specific appliance in use (e.g., Fridge, Oven, Heater).
• Energy Consumption (kWh): Energy consumed by the appliance in kilowatt-hours (kWh).
• Time: The time at which the energy consumption occurred (24-hour format).
• Date: The date when energy consumption was recorded (YYYY-MM-DD).
• Outdoor Temperature (°C): The temperature outside at the time of energy consumption, in degrees Celsius.
• Season: The season of the year (Winter, Summer, Fall, Spring).
• Household Size: Number of people living in the household.

Potential Uses:

This dataset can be utilized in various areas, including:

• Energy consumption forecasting for smart homes.
• Identifying energy-saving opportunities by analyzing usage patterns.
• Understanding the effect of outdoor temperature and seasons on energy usage.
• Developing machine learning models for predicting peak energy demand.
• Appliance-specific energy analysis to evaluate the efficiency of different appliances.
• Studying the impact of household size on energy consumption patterns.

Suggested Analyses:

Energy Consumption by Appliance:

Analyze how much energy different appliances consume and identify the most energy-demanding ones (e.g., Does heating or refrigeration consume more energy?).

Seasonal and Temperature Impact:

Study how energy consumption changes with outdoor temperature and across seasons. For example, does heating increase in winter, and cooling increase in summer?

Energy Usage Patterns by Time of Day:

Explore energy usage trends over the course of the day to identify peak consumption periods (e.g., higher usage in the evening or morning).

Household Size vs. Energy Consumption:

Investigate the relationship between household size and energy consumption. Larger households may show different patterns in appliance usage and energy demand.

Predictive Modeling for Energy Demand:

Build models to predict future energy consumption based on temperature, household size, and appliance usage. This can help energy providers optimize supply and demand.

Energy Efficiency Analysis:

Compare the energy consumption of different households and identify potential areas for improving efficiency. For instance, homes with smaller household sizes may consume more energy per person, indicating possible inefficiencies.

Clustering Homes by Energy Usage:

Group homes based on their energy consumption patterns to identify different lifestyle or energy efficiency clusters.

Acknowledgement

Foto von Ajeet Panesar auf Unsplash

Households in the United States consumed approximately 18.45 quadrillion British thermal units in 2024. This was a slight increase compared to the previous year. Energy consumption by the residential sector peaked in 2010, at 21 quadrillion British thermal units.

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).

This table contains 1155 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; ...); Type of dwelling (7 items: Single-detached; Double; Row or terrace; Duplex; ...); 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).

Dataset and details can be found at the US Energy Information Admininstration (EIA)'s RBECs website

Context

EIA administers the Residential Energy Consumption Survey (RECS) to a nationally representative sample of housing units. Traditionally, specially trained interviewers collect energy characteristics on the housing unit, usage patterns, and household demographics. For the 2015 survey cycle, EIA used Web and mail forms, in addition to in-person interviews, to collect detailed information on household energy characteristics. This information is combined with data from energy suppliers to these homes to estimate energy costs and usage for heating, cooling, appliances and other end uses — information critical to meeting future energy demand and improving efficiency and building design.

First conducted in 1978, the fourteenth RECS collected data from more than 5,600 households in housing units statistically selected to represent the 118.2 million housing units that are occupied as a primary residence. Data from the 2015 RECS are tabulated by geography and for particularly characteristics, such as housing unit type and income, that are of particular interest to energy analysis.

The results of each RECS include data tables, a microdata file, and a series of reports. Data tables are generally organized across two headings; "Household Characteristics" and "Consumption & Expenditures." See RECS data tables.

The RECS and many of the EIA supplier surveys are integral ingredients for some of EIA's more comprehensive data products and reports, such as the Annual Energy Outlook (AEO) and Monthly Energy Review (MER). These products allow for broader comparisons across sectors, as well as projections of future consumption trends.

Content

The Residential Energy Consumption Survey (RECS) is a periodic study conducted by the U.S. Energy Information Administration (EIA) that provides detailed information about energy usage in U.S. homes. RECS is a multi-year effort (Figure 1) consisting of a Household Survey phase, data collection from household energy suppliers, and end-use consumption and expenditures estimation.

The Household Survey collects data on energy-related characteristics and usage patterns of a national representative sample of housing units. The Energy Supplier Survey (ESS) collects data on how much electricity, natural gas, propane/LPG, fuel oil, and kerosene were consumed in the sampled housing units during the reference year. It also collects data on actual dollar amounts spent on these energy sources.

EIA uses models (energy engineering-based models in the 2015 survey and non-linear statistical models in past RECS) to produce consumption and expenditures estimates for heating, cooling, refrigeration, and other end uses in all housing units occupied as a primary residence in the United States. Originally conducted by trained interviewers with paper and pencil, the 2015 study used a combination of computer-assisted personal interview (CAPI), web, and mail modes to collect data for the Household and Energy Supplier Surveys.

Banner image credit: https://www.flickr.com/photos/caribb/1518299093

Dataset from Energy Market Authority. For more information, visit https://data.gov.sg/datasets/d_634194a40f36e5bc11a942ab0164fa9d/view

Dataset

This dataset was created by Hanish Paturi

Contents

Household electricity consumption in the UK has been annually declining for most of the century. Households in the United Kingdom used to consume over 100 terawatt-hours of electricity every year. However, in 2022, domestic electricity consumption dropped below 100 terawatt-hours and it amounted to approximately 92 terawatt-hours in 2023.

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.

This 2009 version represents the 13th iteration of the RECS program. First conducted in 1978, the Residential Energy Consumption Survey is a national sample survey that collects energy-related data for housing units occupied as a primary residence and the households that live in them. Data were collected from 12,083 households selected at random using a complex multistage, area-probability sample design. The sample represents 113.6 million U.S. households, the Census Bureau's statistical estimate for all occupied housing units in 2009 derived from their American Community Survey (ACS).

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.

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.

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.

The dataset encompasses information obtained from energy consumption in a residence located in the northeast region of Mexico over a span of 14 months. Each data point was recorded at one-minute intervals and features energy-related metrics enriched with weather data provided by OpenWeather, under a free license. The variable descriptions are outlined as follows: date active_power current voltage reactive_power apparent_power power_factor main (categorical weather conditions) description (detailed categorical weather conditions) temp feels_like temp_min temp_max pressure humidity speed (wind speed) deg (wind degree) temp_t+1 (forecast of temperature for the next day) feels_like_t+1 (forecast of feels like for the next day) This dataset provides a comprehensive insight into the energy dynamics of the household, coupled with meteorological influences, offering a nuanced perspective on energy consumption patterns over time.

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.

In 2023, household electricity in Singapore amounted to approximately eight terawatt hours. Singapore households made up a small share of the total electricity consumption in that year, which amounted to around 55 terawatt hours.

comprehensive datasets.