National Grid ESO is the electricity system operator for Great Britain. They have gathered information of the electricity demand in Great Britain from 2009. The is updated twice an hour, which means 48 entries per day. This makes this dataset ideal for time series forecasting.

File information

The dataset consists of three type of files: - Historic_demand_year_20xx.csv: electricity demand in that year - Historic_demand_year_2009_2024.csv: all the yearly datasets merged in one - Historic_demand_year_2009_2024_noNaN.csv: same as above, but NaN values have been removed and the date includes the hour as opposed to only the day

Columns

The columns in the dataset are: * SETTLEMET_DATA: date in format dd/mm/yyyy * SETTLEMENT_PERIOD: half hourly period for the historic outtunr occurred * ND (National Demand). National Demand is the sum of metered generation, but excludes generation required to meet station load, pump storage pumping and interconnector exports. National Demand is calculated as a sum of generation based on National Grid ESO operational generation metering. Measured in MW. * TSD (Transmission System Demand). Transmission System Demand is equal to the ND plus the additional generation required to meet station load, pump storage pumping and interconnector exports. Measured in MW. * ENGLAND_WALES_DEMAND. England and Wales Demand, as ND above but on an England and Wales basis. Measured in MW. * EMBEDDED_WIND_GENERATION. This is an estimate of the GB wind generation from wind farms which do not have Transmission System metering installed. These wind farms are embedded in the distribution network and invisible to National Grid ESO. Their effect is to suppress the electricity demand during periods of high wind. The true output of these generators is not known so an estimate is provided based on National Grid ESO’s best model. Measured in MW. * EMBEDDED_WIND_CAPACITY. This is National Grid ESO’s best view of the installed embedded wind capacity in GB. This is based on publicly available information compiled from a variety of sources and is not the definitive view. It is consistent with the generation estimate provided above. Measured in MW * EMBEDDED_SOLAR_GENERATION. This is an estimate of the GB solar generation from PV panels. These are embedded in the distribution network and invisible to National Grid ESO. Their effect is to suppress the electricity demand during periods of high radiation. The true output of these generators is not known so an estimate is provided based on National Grid ESO’s best model. Measured in MW. * EMBEDDED_SOLAR_CAPACITY. As embedded wind capacity above, but for solar generation. Measured in MW. * NON_BM_STOR (Non-Balancing Mechanism SHort-Term Operating Reserve). For units that are not included in the ND generator definition. This can be in the form of generation or demand reduction. Measured in MW. * PUMP_STORAGE_PUMPING. The demand due to pumping at hydro pump storage units; the -ve signifies pumping load. * IFA_FLOW (IFA Interconnector Flow). The flow on on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * IFA2_FLOW (IFA Interconnector Flow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * MOYLE_FLOW (Moyle Interconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * EAST_WEST_FLOW (East West Innterconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * NEMO_FLOW (Nemo Interconnector FLow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * NSL_FLOW (North Sea Link Interconnector Flow). The flow on the respective interconnector. -ve signifies export power out from GB; +ve signifies import power into GB. Measured in MW. * ELCLINK_FLOW. Blank

The UK's direct use of energy from fossil fuels and other sources (nuclear, net imports, renewables, biofuels and waste and reallocated use of energy by industry (SIC 2007 section - 21 categories), 1990 to 2023.

[v2 update] weather data correction

The data describes an electrical energy community, containing photovoltaic (PV) production profiles and end-user consumption profiles, desegregated by individual appliances used.

A dataset of a residential community was constructed based on real data, where sample consumption and photovoltaic generation profiles were attributed to 50 residential households and a public building (municipal library), a total of 51 buildings. The data concerns a full year.

The overall power consumption of these houses was desegregated into the consumption of 10 commonly used appliances using real energy profiles.

This work has been published in Elsevier's Data in Brief journal: Calvin Goncalves, Ruben Barreto, Pedro Faria, Luis Gomes, Zita Vale, Dataset of an energy community's generation and consumption with appliance allocation, Data in Brief, Volume 45, 2022, 108590, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2022.108590 (https://www.sciencedirect.com/science/article/pii/S2352340922007971)

We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Data in Brief publication to cite this work.

Reference data used to create this dataset:

Renewable energy production profiles: https://site.ieee.org/pes-iss/data-sets/

End-user profiles:

https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households

https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption

https://site.ieee.org/pes-iss/data-sets/

Energy consumption readings for a sample of 5,567 London Households that took part in the UK Power Networks led Low Carbon London project between November 2011 and February 2014.

Readings were taken at half hourly intervals. Households have been allocated to a CACI Acorn group (2010). The customers in the trial were recruited as a balanced sample representative of the Greater London population.

The dataset contains energy consumption, in kWh (per half hour), unique household identifier, date and time, and CACI Acorn group. The CSV file is around 10GB when unzipped and contains around 167million rows.

Within the data set are two groups of customers. The first is a sub-group, of approximately 1100 customers, who were subjected to Dynamic Time of Use (dToU) energy prices throughout the 2013 calendar year period. The tariff prices were given a day ahead via the Smart Meter IHD (In Home Display) or text message to mobile phone. Customers were issued High (67.20p/kWh), Low (3.99p/kWh) or normal (11.76p/kWh) price signals and the times of day these applied. The dates/times and the price signal schedule is availaible as part of this dataset. All non-Time of Use customers were on a flat rate tariff of 14.228pence/kWh.

The signals given were designed to be representative of the types of signal that may be used in the future to manage both high renewable generation (supply following) operation and also test the potential to use high price signals to reduce stress on local distribution grids during periods of stress.

The remaining sample of approximately 4500 customers energy consumption readings were not subject to the dToU tariff.

More information can be found on the Low Carbon London webpage

Some analysis of this data can be seen here.

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 United Kingdom’s electricity use has been declining since peaking at *** terawatt-hours in 2005. In 2024, the UK's electricity increased on the previous year, amounting to *** terawatt-hours. Electricity consumption in the UK typically follows a seasonal trend, peaking in the winter months. How electricity-intensive is the UK? Despite the continual decline in electricity consumption, the UK remains one of the largest electricity consumers in the world. In terms of per capita electricity consumption, however, the UK ranks low in comparison to other European countries such as Norway, Germany, and France. In 2023, it registered an average of ***** kilowatt-hours per person. The race towards a clean power mix In 2010, gas and coal accounted for roughly ** percent of the UK's power mix. Since then, alongside the EU Renewables Directive, the UK agreed and created its own National Renewable Energy Plan, to increase the use of renewable sources and decrease its fossil fuel dependence. In the past decade, the share of energy consumption in the UK attributable to renewable energy increased slightly, although it was still a small percentage out of the total in 2023.

The Household Electricity Disaggregation dataset provides detailed, appliance-level insights into how households use electricity. Each record corresponds to a single household (user_id) and captures the energy consumed in a specific category during a given period, measured in kilowatt-hours (kWh) and as a percentage of total household electricity.

Key features include: 1. user_id: Unique anonymised identifier for each household, enabling cross-linkage with other datasets. 2. created_at: Timestamp indicating when the disaggregation record was created. 3. id: Unique record identifier. 4. period_type & period: Aggregation period (e.g., month) and specific month of observation. 5. type: Electricity consumption (elec). 6. category: Appliance or usage type, including lighting, cooking, washing, hot water, entertainment, refrigeration, always-on devices, and heating. 7. energy (kWh): Absolute electricity consumption for the category. 8. percentage (%): Share of household electricity consumed by that category; summing all categories per household equals 100%.

The dataset is exclusively electricity-focused and provides insights into household energy behaviour, enabling: - Appliance-level analysis: Understand which categories drive consumption in different households. - Segmentation & profiling: Group households based on dominant energy uses, e.g., EV charging, always-on devices, or high entertainment load. - Energy efficiency evaluation: Identify opportunities to reduce consumption in specific categories. - Behavioural insights: Study patterns like time-of-year changes in usage or appliance adoption trends. - Demand forecasting & modelling: Integrate with half-hourly electricity consumption datasets to enhance predictive models.

This dataset is anonymised and suitable for commercial, research, and policy applications, providing a rich resource for understanding residential electricity consumption at a granular, category-specific level.

The IDEAL Household Energy Dataset comprises data from 255 UK homes. Alongside electric and gas data from each home the corpus contains individual room temperature and humidity readings and temperature readings from the boiler. For 39 of the 255 homes more detailed data is available, including individual electrical appliance use data, and data on individual radiators. Sensor data is augmented by anonymised survey data and metadata including occupant demographics, self-reported energy awareness and attitudes, and building, room and appliance characteristics. The 00README.txt download summarizes the contents of the other files.

This dataset is a cleaned up version of the London dataset. This dataset was create to do forecasting on

This is a set of aggregated data tables that underlie the key figures in the SERL statistical report "Smart Energy Research Lab: Energy use in GB domestic buildings 2022 and 2023". The report – Volume 2 of the SERL Statistical Reports Series – describes domestic gas and electricity energy use in Great Britain in 2022 and 2023 based on data from the Smart Energy Research Lab (SERL) Observatory, which consists of smart meter and contextual data from approximately 13,000 homes that are broadly representative of the GB domestic building stock along a range of geographic, building and socio-demographic characteristics. The report provides an update to the statistics provided in Volume 1 of the SERL Statistical Report Series (Few et al., 2022), which covered 2021 data, and analyses residential energy use in GB in 2022 and 2023 (over the whole year, in each month and half-hourly over the course of the day). Statistics are presented for groups of homes with specific occupant characteristics (number of occupants, tenure), property characteristics (age, size, form, and Energy Performance Certificate (EPC)), heating systems, photovoltaics and electric vehicles, and by weather, region and IMD quintile. Unless otherwise noted, the findings in the report relate to homes in the SERL Observatory that use gas as their main heating source and do not have photovoltaic (PV) electricity generation.The report also shows how metered residential energy use in GB varies over time from 2021 to 2023.

This dataset is a specialised subset of Half-Hourly Electricity Consumption Data | UK Coverage, focusing on households with heat pumps. It currently includes 1,460+ households, with 880+ having 12 months or more of continuous readings, and the number increasing monthly.

All data fields and structure are identical to the main dataset (30-minute intervals, kWh values, smart-meter compatible). This subset supports focused analysis of electrified heating demand, seasonal usage patterns, and the impact of heat pump adoption on household energy consumption and grid load.

Electricity consumption by households in the UK, 2000 to 2008

The Modernising Energy Data Access (MEDA) competition was set up by Innovate UK and the Modernising Energy Data group to help develop the concept of a Common Data Architecture (CDA) for the Energy Sector. One of the main goals of the Common Data Architecture is to improve data sharing across the energy sector and make data more interoperable across organisations. Energy Consumption is one of the most sought after datasets needed by the organisations that we have worked with throughout a variety of the Modernising Energy Data projects, and although getting a household level of this information comes against GDPR challenges and is therefore non-accessible for the vast majority of organisations, breaking consumption down into smaller areas can be hugely beneficial for gaining insights into how energy is consumed within the UK. We have amalgamated Gas and Electricity consumption per Lower Layer Super Output Area (LSOA) which is available to download via file transfer, or via API

This dataset provides ultra-high-resolution electricity power readings from over 15,500 UK households equipped with smart meter-connected Consumer Access Devices (CADs). Each device records instantaneous power readings every 10 seconds (in watts), offering both real-time monitoring and long-term historical data spanning up to 12 months.

Each record includes: 1. user_id – anonymised household identifier 2. reading_timestamp – precise time of power measurement 3. reading (watts) – instantaneous power draw

Ideal for demand forecasting, load profiling, appliance detection, grid modelling, and energy efficiency research, this dataset enables detailed insight into real-world electricity consumption patterns.

All data is collected with explicit user consent, fully GDPR-compliant, and the dataset continues to grow daily as more CAD-enabled households join the network.

Context

The dataset featured below was created by aggregating hourly energy consumption data from individual London homes provided by UK Power Networks. The dataset keeps track of the energy consumption of 5,567 randomly selected households in London from November 2011 to February 2014.

-> This energy dataset is a great addition to this London Weather Dataset. You can join both datasets on the 'date' attribute, after some preprocessing, and perform some interesting data analytics regarding how energy consumption was impacted by the weather in London.

Content

The size for the file featured within this Kaggle dataset is shown below — along with a list of attributes and their description summaries: - london_energy.csv - 3510433 observations x 3 attributes

1. LCLid - individual household unique identifier - (str)
2. Date - date of recorded measurement - (date)
3. KWH - energy consumption measurement in kWh - (float)

Source

Energy Data - https://data.london.gov.uk/dataset/smartmeter-energy-use-data-in-london-households