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The Centre for Sustainable Energy (CSE) Fuel Poverty Calculator has been updated to account for the new definition of fuel poverty: Low Income Low Energy Efficiency (LILEE).

Fuel poverty is when people have to spend a high proportion of their income to keep their home warm due to a combination of poor housing with inadequate insulation and heating, expensive energy tariffs, and low incomes. One outcome for those in fuel poverty is poor physical and mental health. NICE, which provides evidence-based guidance for NHS and social care professionals, recognises that living in a cold home will make most health conditions worse from childhood asthma to heart disease.

The Customer-Led Network Revolution project assessed the potential of novel smart grid network technologies, new commercial arrangements and customer flexibility solutions to find cost-effective ways to prepare UK electricity networks for the future. Datasets include Enhanced profiling of domestic customers electric hot water immersion heating & storage heating; Real-Time Thermal Rating for Extra High Voltage Underground Cables; Enhanced Automatic Voltage Control for Low Voltage Network Regulator; and many more Many resources available including datasets, analysis, reports and videos. The datasets are in the Project Library: search for Dataset to find them, including an overview guide to the datasets.

The data is from efficiency tests performed at The University of Sheffield, on a 1MWh/2MW lithium-titanate battery energy storage (BESS) by Toshiba SCiB Technology, within the EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project EP/N032888/1. The system is connected directly to the grid through an 11kV feed, located at Willenhall Primary Substation. Interfacing to grid is achieved through a 2.2MVA ABB PCS100 inverter, and a 2.1MVA dry-type transformer with 1:30.55 ratio. This data was collected as part of an experiment within the MANIFEST project to study the efficiency of the Willenhall Energy Storage System, managed by The University of Sheffield.The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.The data presented here has contributed to the following publications:'Experimental Analysis of Efficiencies of a Large Scale Energy Storage System', S. Mori, M. J. Smith, D. T. Gladwin & D. A. Stone, 'Practical state estimation using Kalman filter methods for large-scale battery systems', Z. Wang, D. T. Gladwin, M. J. Smith, S. Haass (Under review)

The experiments were conducted using an Energy Storage System (ESS) in the Smart Grid laboratory at Newcastle University that includes transformers and converters interfacing to the grid, and various energy storage assets. The aim was to demonstrate the control of power flows between the grid and a 90kW / 2kWh supercapacitor bank. Tests were performed to control real power steps (short duration / high power, and long duration / low power) and reactive power steps of the grid-coupled converter. The corresponding power time-series at the supercapacitor bank terminals and the State of Charge (SoC) were recorded and plotted in the accompanying report.

The data were collected as part of an experiment within the MANIFEST project to demonstrate control operations of an ESS using the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

In this experiment the efficiency of an emulated Energy Storage System (ESS) is measured in the Smart Grid laboratory at Newcastle University. Real-time simulation is carried out using a TRIPHASE real-time simulator to model the ESS network, allowing the laboratory hardware to operate as though it is coupled to a real distribution-scale network. The ESS emulator was used to represent the characteristics of three Li-ion battery banks with nominal voltage, power and energy ratings as follows: (i) 200V 1kW 1kWh, (ii) 400V 5kW 5kWh, and (iii) 400V 10kW 10kWh.

The data were collected as part of an experiment within the MANIFEST project to study the efficiency of an emulated supercapacitor using the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

In this experiment the response time of an Energy Storage System (ESS) was measured in the Smart Grid laboratory at Newcastle University. Real-time simulation was carried out using a TRIPHASE real-time simulator to model the ESS network, allowing the laboratory hardware to operate as though it is coupled to a real distribution-scale network. The ESS emulator was used to represent the characteristics of a Li-Ion battery with a single string of 50 series cells, each with a nominal voltage of 4 V and a capacity of 200 Ah; these parameters were selected in order to set the nominal voltage, power and energy ratings of the ESS to 200 V, 20 kW and 40 kWh respectively.

The data were collected as part of an experiment within the MANIFEST project to study the response time of an ESS using the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

The experiments were conducted using an Energy Storage System (ESS) in the Smart Grid laboratory at Newcastle University, that includes transformers and converters interfacing to the grid, and various energy storage assets. They tested the efficiency of the system components, including the supercapacitor bank and associated power converters. Tests were performed to control the supercapacitor power steps, while measuring power at the terminals of each system component. Efficiencies of the system components for various initial values of supercapacitor state of charge (SoC) are presented in the data files and accompanying report.

The data were collected as part of an experiment within the MANIFEST project to demonstrate the efficiency of the supercapacitor energy storage system and associated power converters in the Smart Grid Laboratory at Newcastle University, UK.

The EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1) aimed to study and investigate the capabilities of energy storage systems installed in UK Universities.

Electricity North West is a distribution network operator. Data available includes network data, network assets, capacity and embedded generation. Electricity North West is a distribution network operator (DNO) covering the north-west of England.

The English House Condition Survey (EHCS) was a national survey of housing in England. It was originally called the National House Condition Survey and covered England and Wales. From April 2008, the EHCS merged with the Survey of English Housing (SEH) to form the English Housing Survey (EHS). The English House Condition Survey (EHCS) was a national survey of housing in England, commissioned by the Ministry of Housing, Communities and Local Government (MHCLG). The survey was originally called the National House Condition Survey and covered England and Wales. The EHCS ran quinquennially from 1967-2001. From 2002, the survey moved to a continuous basis and the data were provided as a two-year rolling sample of approximately 16,000 cases. From April 2008, the EHCS merged with the Survey of English Housing (SEH) to form the English Housing Survey (EHS).

The English Housing Survey (EHS) began in 2008 as a continuous national survey collecting information about peoples housing circumstances and the condition and energy efficiency of housing in England. The EHS brought together two previous survey series - the English House Condition Survey (EHCS) and the Survey of English Housing (SEH) - into a single fieldwork operation. The EHS covers all housing tenures and provides valuable information and evidence to inform the development and monitoring of government housing policies. The survey has a complex multi-stage methodology consisting of 3 main elements: an initial interview survey of around 17,000 households with a follow-up physical inspection and a desk-based market valuation of a sub-sample of 8,000 of these dwellings, including vacant dwellings. The interview survey sample forms part of the Integrated Household Survey (IHS), and the core questions from the IHS form part of the EHS questionnaire.

The English Housing Survey (EHS), 2012: Fuel Poverty Dataset is derived from the 2012 EHS database. The dataset is the outcome of analysis conducted to produce estimates of the number of households living in fuel poverty in England in 2012. Previously, a household was defined as being fuel poor if they spent more than 10% of their income on fuel. The English Housing Survey, 2012: Fuel Poverty Dataset is derived from the 2012 EHS database created by the DCLG. This database is constructed from fieldwork carried out between April 2011 and March 2013. The midpoint of this period is April 2012, which can be considered as the reference date for the fuel poverty dataset.

Statistics relating to the demographics of fuel poverty in England, where fuel poverty is defined by the Low Income / High Costs balance, which considers a household to be in fuel poverty if: (i) the household has required fuel costs above average (national median level) and (ii) was the household to spend that amount it would be left with a residual income below the official poverty line. The key drivers behind fuel poverty are:
The energy efficiency of the property (and therefore, the energy required to heat and power the home);
The cost of energy;
Household income.
Registration with the UK Data Archive is required before data can be accessed

This data collection includes a User Guide and the anonymised transcripts of 30 semi-structured interviews of #60-90 minutes each, with 31 high household energy consumers, about their homes, appliances, infrastructures, vehicles, and everyday life and travel practices that generate household (domestic and travel-related) energy demand. It also includes the anonymised transcripts of 4 3-hour deliberatively workshops of ~8 public participants and 2 facilitators, one recruited from the interviewees, and the others recruited to represent different levels of domestic and travel-related energy consumption, held to discuss the validity, fairness, effectively and acceptability of four broad policy approaches to reduce (particularly high levels of) household energy consumption: Rationing; Economic (Dis)Incentives; Structural Change; and Behaviour Change

The data are the results of tests carried out in a project funded by the Energy Technologies Institute (ETI) to model at reduced scale, the consequences of a flameout in a full-size combined cycle gas turbine (CCGT) when running on high hydrogen fuel mixtures. The High Hydrogen data set includes data from 69 Circular Duct Tests and 76 tests with a Heat Recovery Steam Generator. The data are further described by read me files in each directory and by reports in the directory Supporting_Documentation.
Phase 1 data (Circular Duct tests 1 - 69 and Heat Recovery Steam Generator tests 1 to 17) were added in December 2017. Phase 2 data (Heat Recovery Steam Generator tests 18 to 76) were added in December 2019, when this catalogue entry was updated accordingly.

The HES measured electricity consumption at an appliance level in 250 owner-occupied households across England from 2010 to 2011, the most detailed monitoring of electricity use ever carried out in the UK. Extensive analysis has been undertaken on appliance ownership, labels, actual efficiencies and potential for savings in different social groups. The study has also examined the scope for reducing peak electricity demand. The full dataset is not available via the web. A sample, and numerous reports and analyses are freely available. There are also three spreadsheet tools provided.
The Household Electricity Survey (HES) was the most detailed monitoring of electricity use ever carried out in the UK. Electricity consumption was monitored at an appliance level in 250 owner-occupied households across England from 2010 to 2011. Extensive analysis has been undertaken on appliance ownership, labels, actual efficiencies, potential for savings in different social groups and the potential to reduce peak electricity demand.

The Community Energy Saving Programme (CESP) was designed to target the lowest income households living in homes that need significant investment in energy saving measures and low carbon technologies. CSE mined the CESP data stores to develop and publish a database of useful information to assist with this targeting. Ofgem CESP programme page:
https://www.ofgem.gov.uk/environmental-programmes/energy-companies-obligation-eco/previous-energy-efficiency-schemes
CESP Report location:
https://www.ofgem.gov.uk/ofgem-publications/58763/cesp-final-report-2013final-300413.pdf
For super output area (SOA) definitions:

http://www.ons.gov.uk/ons/guide-method/geography/beginner-s-guide/census/super-output-areas--soas-/index.html

The MAGIC website provides geographic information about the natural environment from across government. The information covers rural, urban, coastal and marine environments across Great Britain. It is presented in an interactive map which can be explored using various mapping tools that are included. Datasets in MAGIC have licences depending on their origin. The map images are provided by Ordnance Survey and subject to Crown Copyright. Further information about copyright is found on the MAGIC site under About, Legal Documentation, Copyright information. Further information about dataset licences is found on the Dataset and Download Listings page (a link in the Copyright information page).

Hourly energy consumption data gathered from 94 (originally 160) low energy homes in Milton Keynes Energy Park (1989-1991). These homes were of conventional design for the UK (35 different designs) but included energy efficiency features (primarily increased insulation in the roof, wall, and flooring and energy efficient boilers) so that they correspond with SAP values of 75-90. The results were used to generate a database of energy consumption data to assist in development of the Building Research Establishment Domestic Energy Model (BREDEM). The ReadMe_MiltonKeynes.html file contains more detailed information about the datasets. Registration with EDC is required for access to the data.
This data was originally collected by the Building Research Establishment (BRE) during a project in 1988-91 in an area then known as Milton Keynes Energy Park, located some 75 km north-west of London. The study monitored more than 100 sites for hourly electricity and gas consumption; the dwellings essentially follow conventional UK housing design, but were built in 1987 to higher standards for energy performance than were required by the building regulations at that time (Edwards, 1990). They incorporated energy efficiency features, such as increased floor, wall, and loft insulation, double-glazing, and in some cases condensing boilers, so that they broadly corresponded to UK building standards of almost a decade later.
The data has been rescued off the original 5.25 inch floppy disks by Steve Pretlove of UCL towards his PhD thesis and has since been cleaned and further processed by Alex Summerfield. Data were successfully retrieved for 94 sites with more than 200 days of valid date each. Some building data, including floor areas and U-values were recovered, unfortunately related social data, such as the number of occupants in each household, were not available.
Reference: J. Edwards (1990), Low energy dwellings in the Milton Keynes Energy Park, Energy Management v26 p32-33.
See also: numerous internal Buildings Research Establishment (BRE) reports.
Note that there are some missing data points largely due to problems of recovery from the original media (the data from July 1989 are missing from many data sets) but nonetheless there are many sites with > 12 months of data.

Automated Demand Response trials within the New Thames Valley Vision (NTVV) project installed Building Management Systems within 27 commercial buildings within the Bracknell area. The project was then able to enact load shed events on the building with differing notice periods. Background information on the NTVV project and the ADR trial can be found in report SDRC_9.1c_Demand_Side_Response_Evidence_Report_1.0.pdf

The data set records EMMA thermal storage devices trialed in the New Thames Valley Vision (NTVV) project and includes PV and grid data for 73 households. The EMMA unit is a device which is fitted in conjunction with Solar PV installs in properties that have an immersion heater. The device diverts any excess generation energy, into the immersion heater to heat water, rather than send the excess electricity back onto the grid. The data set records EMMA, PV and grid data for 73 households.
Background information on the NTVV project and the EMMA trial can be found in report SDRC_9.4b_Evidence_Report_1.0.pdf.
Data format information is in EMMA_readme.txt.

End point monitoring of household electricity demand and generation carried out within the New Thames Valley Vision (NTVV) project. End Point Monitoring in domestic households was carried out with two types of devices with 30 minute granularity. The data are described in EPM_readme.txt.
Background information on the NTVV project and end point monitoring with the EDMI units can be found in SDRC_9.2a_Evidence_Report_Rev_1.0_Final.pdf.

Data from a trial of an Ice Bear cold thermal energy storage unit in one building during the New Thames Valley Vision (NTVV) project. This data records the trial of one Ice Bear unit in the New Thames Valley Vision (NTVV) project. The Ice Bear unit provides a block of ice, which is installed as part of an air conditioning system. During the day, the air conditioning unit will blow air through the ice, creating cool air. During the night, the Ice Bear will freeze the ice again, ready for use the next day, so deferring energy usage from the day time, to the night time.
The data format is described in Ice_Bear_readme.txt.
Background information on the NTVV project and the Ice Bear unit can be found in report SDRC_9.8a_4_Cold_Thermal_LV_Network_Energy_Storage.pdf.

Monitoring data from 4 primary and 316 secondary substations in the New Thames Valley Vision (NTVV) project. Information about the substations data can be found in Substations_readme.txt.
Background information on the NTVV project and substation monitoring can be found in SDRC_9.2b_Evidence_Report_Rev_1.0.pdf.

Northern Power Grid is a distribution network operator. Their network data includes Distribution Future Energy Scenarios, Auto Design connection tool, embedded generation capacity register, long-term development statement (including network information), generation and demand heat maps, and contracted capacity register. Northern Power Grid is a distribution network operator (DNO) covering north east England, Yorkshire and north Lincolnshire.

Electricity data measured at one-minute resolution in 22 dwellings over two complete years (2008 and 2009). Each dwelling was fitted with a single meter covering electricity use of the whole dwelling, however, the one-minute resolution allows the switching of individual domestic appliances to be observed. The meters conform to BS EN 62053-21:2003 and measure true active power (taking proper account of the voltage). The study also includes the results of a dwelling characteristics survey which was completed for each dwelling detailing its characteristics and the types of appliances and lighting installed. The data are separated by dwelling (22) and year (2) so there are 44 data files of one-minute resolution domestic electricity use. A short survey of dwelling characteristics is includes; the main topics covered are: general energy use; lighting; and available appliances.

The Place-based Carbon Calculator (PBCC) is a free tool which estimates the per-person carbon footprint for every Lower Super Output Area (LSOA) in England. LSOAs are small statistical areas with a population of about 1,500 - 3,000. Includes data on housing, transport and other consumption emissions. Bulk download data and further information about the data sets are available at https://www.carbon.place/data/.

Department for Levelling Up, Housing & Communities publishes statistics relating to deprivation, housing and homelessness, local government finance, planning performance and land use.DLUHC was previously known as the Ministry of Housing, Communities and Local Government (MHCLG) Statistical collections on deprivation, housing, local government finance, and planning and land use are available.

The Survey of English Housing (SEH) was a continuous annual survey series, which began in 1993 and covered a sample of 20,000 responding households each year. The survey provided key housing data on tenure, owner occupation and the social rented sector, and regular information about the private rented sector. It was a multi-purpose housing survey which provided a comprehensive range of basic information on households and their housing and full information on the private rented sector. The SEH was subsumed into the English Housing Survey (EHS) in 2008. Citation is
;National Centre for Social Research and Department for Communities and Local Government, Survey of English Housing, 1993/94-2007/08 (computer file). 2nd Edition. Colchester, Essex: UK Data Archive (distributor), June 2010. SN: 6376, http://dx.doi.org/10.5255/UKDA-SN-6376-1

High speed (16 kHz) power meter data for 3 houses.
Data is stored in individual directories for each house.

NOTE to UK-DALE data users: the UK-DALE data sets are large. It may not be possible to download all the data in one go.

See Kelly, J. and Knottenbelt, W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2:150007 doi: 10.1038/sdata.2015.7 (2015). arXiv:1404.0284 (2015)
This data is made freely available under Creative Commons Attribution 4.0 International (CC BY 4.0). See more at https://creativecommons.org/licenses/by/4.0/

Appliance-by-appliance and whole-home power demand for 5 UK homes recorded approximately once every 6s. For 3 of the homes, whole-home data was also recorded at 1s and 16kHz
Data is stored in individual directories for each house. Each appliance has a separate time series file channel_nn.dat and most have a channel_nn_button_press.dat file indicating switching events (see Other Information).

NOTE to UK-DALE data users: the UK-DALE data sets are large. It may not be possible to download all the data in one go.

See Kelly, J. and Knottenbelt, W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2:150007 doi: 10.1038/sdata.2015.7 (2015). arXiv:1404.0284 (2015)
This data is made freely available under Creative Commons Attribution 4.0 International (CC BY 4.0). See more at https://creativecommons.org/licenses/by/4.0/

High speed (16 kHz) whole-house voltage and current data for 1 house. Data is stored in individual directories for each week. This dataset continues UK-DALE-2015 : UK-DALE-16kHz

NOTE to UK-DALE data users: the UK-DALE data sets are large. It may not be possible to download all the data in one go.

This data set continues UK-DALE-2015 : UK-DALE-16kHz
There is further information on UK-DALE-2015, which is also relevant to this data set, in Kelly, J. and Knottenbelt, W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2:150007 doi: 10.1038/sdata.2015.7 (2015). arXiv:1404.0284 (2015)
This data is made freely available under Creative Commons Attribution 4.0 International (CC BY 4.0). See more at https://creativecommons.org/licenses/by/4.0/
There is a README associated with these data at ReadMe_DALE-2017 (#HighSpeed section).

Appliance-by-appliance and whole-home power demand for 5 UK homes recorded approximately once every 6s. Each appliance has a separate time series file channel_nn.dat and most have a channel_nn_button_press.dat file indicating switching events. This data set includes and extends UK-DALE-2015 disaggregated.

NOTE to UK-DALE data users: the UK-DALE data sets are large. It may not be possible to download all the data in one go.

This data set includes and extends UK-DALE-2015 disaggregated. The disaggregated UK-DALE-2015 data are available as separate .dat files. This data set is available as a single zip file of the full set of plain text .dat files or as a single zip of the full set of HDF5 format data.
There is further information on UK-DALE-2015, which is also relevant to this data set, in Kelly, J. and Knottenbelt, W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2:150007 doi: 10.1038/sdata.2015.7 (2015). arXiv:1404.0284 (2015).
This data is made freely available under Creative Commons Attribution 4.0 International (CC BY 4.0). See more at https://creativecommons.org/licenses/by/4.0/.
There is a README associated with these data at ReadMe_DALE-2017 (#Disaggregated section) and further meta data on the houses and appliances at http://data.ukedc.rl.ac.uk/simplebrowse/edc/efficiency/residential/EnergyConsumption/Domestic/UK-DALE-2017/UK-DALE-FULL-disaggregated/metadata.

Data are available for each house as follows:
House 1: start 09/11/2012, end 26/04/2017
House 2: start 17/02/2013, end 10/10/2013
House 3: start 27/02/2013, end 08/04/2013
House 4: start 09/03/2013, end 01/10/2013
House 5: start 29/06/2014, end 13/11/2014

UK Power Networks distribution network operators open data include data about their networks, operations, embedded generation and market intelligence data. UK Power Networks are the distribution network operator (DNO) for parts of eastern and south eastern England and London. Data is open, but new users will need to register.

Meteorological and panel production data from the solar PV project - Southerly Winds Increase the Electricity Generated by Solar Photovoltaic Systems. This project looked at meteorological effects on solar panel electricity output in a field-based setting, demonstrating how differences in exposed surface area under northerly and southerly winds can affect the efficacy of surface cooling with implications for electricity output. This project, Southerly Winds Increase the Electricity Generated by Solar Photovoltaic Systems, is further described in this journal article: https://www.sciencedirect.com/science/article/abs/pii/S0038092X20303303?dgcid=author.