Search Results: Data Location: Stored with the EDC

Half hourly gas and electricity (and water) consumption data ( not all utilities were monitored at every site) gathered during the Carbon Trusts Advanced Metering for SMEs project (2004-2006). The data was collected from a portfolio of sites by seven independent metering contractors. Sites are characterized by the number of employees, the geographical location, and geographical region. (Please acknowledge The Carbon Trust when making any use of this data)
Meter readings are available for small and medium-sized enterprises (SMEs).The majority of meter readings are for electricity; monitoring of other utilities (gas and water) was available on some sites. Unless otherwise indicated data sets are for electricity; where multiple meters were deployed they are designated (e) for electricity, (g) for natural gas, and (w) for water. In a minority of cases metering on the site was split between several meters: (e1), (e2), etc. The absence of a link indicates some problems in matching the site identifier with the collected data set.
The sectors covered are:-

• Community, social and personal activities
• Education
• Financial and business services
• Government
• Health and social work
• Manufacturing
• Other sectors
• Wholesale and retail trade

A ReadMe page is provided for each subsector containing more detailed information for the datasets that are covered. Use of this data resource requires registration with edc: please contact the EDC Manager
The Carbon Trust has published a report summarising the findings of the project at http://www.ieadsm.org/Files/Content/Carbon%20Trust_2011-06-03.pdf and a full report on the advanced metering field trial, its results, findings and recommendations which is available alongside the data at http://data.ukedc.rl.ac.uk/simplebrowse/edc/efficiency/residential/Buildings/AdvancedMeteringTrial_2006/CTC713_AdvancedMeteringForSMEs.pdf.

The characteristics of 5 types of bioenergy feedstock crops were measured in relation to growing conditions and methods.

The Characterisation of Feedstocks Project was commissioned by The Energy Technologies Institute (ETI) and carried out by Forest Research, and Uniper Technologies Ltd (formerly part of E.ON).

Five crops were considered: Miscanthus, willow short-rotation-coppice (SRC), poplar SRC, poplar short-rotation-forestry (SRF), and conifer SRF.

Eight studies were carried out:
In phase 1 (fieldwork spring to autumn 2015):
1. To examine the impact of climate zone, soil type, harvesting time, and storage on Miscanthus, willow SRC, poplar SRC, poplar SRF and conifer SRF
2. To examine the variation between and within fields of Miscanthus and willow SRC
3. To examine the feedstock characteristics of willow SRC and poplar SRF leaves
4. To examine the feedstock characteristics of Miscanthus before and after pelletising
In phase 2 (fieldwork November 2015 to November 2016):
V1. To examine the impact of harvest time on Miscanthus characteristics
V2. To examine the impact of harvest time on willow characteristics
V3. To examine the impact of varieties on willow characteristics
V4. To examine the impact of storage time on Miscanthus characteristics

This is an evidence base of available literature on the impacts of offshore wind farm (OWF) developments and the outcomes for marine ecosystem services. The evidence was collated through a systematic search of global primary literature (also known as peer-reviewed or published literature) and UK grey literature regarding the impacts of OWF developments (the scope for including global grey literature was unfeasible). Grey literature refers to multiple types of report or document, and is defined as: "information produced on all levels of government, academia, business and industry in electronic and print formats not controlled by commercial publishing" i.e. where publishing is not the primary activity of the producing body(ICGL, 1997). Data was extracted from each evidence source, for each subject or marine ecosystem component that was impacted by the OWF development, the phase of development, the specific pressure and other relevant information about the wind farm or location. Expert judgement was used to map each piece of evidence for impacts on the marine environment according to CICES (Common International Classification of Ecosystem Services) or MEA (Millenium Ecosystem Assessment) and other published classification systems for ecosystem services (Ryfield et al., 2019; Hooper et al., 2020). The dataset was created to provide a central evidence base of available primary and grey literature regarding the impacts of OWF developments (for construction, operational and decommissing phases) on ecosystem service outcomes in the marine environment. The primary literature represents a global semi-systematic search, while the grey literature is restricted to a semi-systematic search for reports and documents from the UK (that also include wind farms in adjacent waters). The database provides a comprehensive list of available literature on this topic, and it enables the assessment and comparison of the evidence for enviromental impacts and ecosystem service outcomes between primary and grey literature sources. This will be critical for marine policy makers when gathering evidence for future OWF developments and advances understanding of the impacts and trade-offs associated with OWF and ecosystem services. A tool (currently in development by Plymouth Marine Laboratory) will improve accessibility of the data for this purpose. This research was funded by the UKERC Phase 4 research programme.

Estimated unit energy consumption per annum (kWh/annum) (2006/2007) for various UK domestic cold appliances (chest freezer, fridge-freezer, refrigerator, upright freezer) by appliance energy class NC08 Assumptions underlying the energy projections for domestic cold appliances, Version 2.1 (2006) and Version 3.0 (2007) from DEFRA MTP. See also DEFRA Market Transformation Programme - efficient-products.ghkint.eu/

Estimated ownership of UK domestic washing machines and washer dryers (percentage of households) Historic data suggests that the average annual unit energy consumption for a domestic washing machine in the UK is 268 kWh (1999). It is assumed that a 60C wash uses 40% less energy than a 90C wash; and a 40C wash uses 40% less energy than a 60C wash for estimated number of uses of each cycle). An average load capacity of 5.58 kg in 2006 was assumed, rising by 0.025 kg/year to allow for the future availability of larger capacity machines. Washing machines typically do not adjust water and energy consumption if a smaller load is washed. Average water consumption per wash was estimated to be 50 litres (2006); 80% of energy consumption is for heating water in the wash phase.
It is assumed that the best available appliances will be A (2006-2009) and A+ (2010-2020).
BNW05 Assumptions underlying the energy and water projections for washing machines, Version 2.0 from Defra MTP; data based on Gfk and historical modelling by DECADE and Fawcett, T., Lane K., Boardman, B., et al, 2000, Lower Carbon Futures for European Households, Environmental Change Institute Oxford Appendix F, UK 1 Table 1 (using data from the English Housing Condition Survey)

Estimated unit energy consumption per cycle (2006) for UK domestic washing machines by appliance energy class Historic data suggests that the average annual unit energy consumption for a domestic washing machine in the UK is 268 kWh (1999). It is assumed that a 60C wash uses 40% less energy than a 90C wash; and a 40C wash uses 40% less energy than a 60C wash. An average load capacity of 5.58 kg in 2006 was assumed, rising by 0.025 kg/year to allow for the future availability of larger capacity machines. Washing machines typically do not adjust water and energy consumption if a smaller load is washed. Average water consumption per wash was estimated to be 50 litres (2006); 80% of energy consumption is for heating water in the wash phase.
It is assumed that the best available appliances will be A (2006-2009) and A+ (2010-2020).

The data is from 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. The main parameters are related to time-series of grid voltage and frequency, AC and DC voltages and currents, operating conditions (such as for example room temperature), etc.

The experimental background is explained in Experiment_Description_Readme.pdf

The data are time-stamped in the format MM/DD/YYYY HH:MM:SS UTC, while the parameters are given using the International System of Units.
This catalogue entry was updated on 2021-02-03 to link directly to the data from the calibration test carried out on 2017-03-01. Previously it linked to the top level of the Sheffield BESS test data at https://data.ukedc.rl.ac.uk/browse/edc/power/storage/MANIFEST/Sheffield. The title was updated to include the words "Calibration Test" to differentiate it from other tests carried out with the Sheffield BESS facility. The previous title was "EPSRC ManIfest Battery Energy Storage System (BESS) The University of Sheffield". No changes have been made to the dataset and no other changes have been made to this catalogue record.

The data was collected during instantaneous power loss and efficiency tests performed at The University of Manchester in 2018 and 2020 , on a 240kVA and 180kWh Siemens SieStorage lithium-ion battery energy storage system (BESS). The system is connected behind the meter to the low-voltage distribution network via a 260kVA, 400/433V TMC isolation transformer. This data was collected as part of an experiment within the MANIFEST project.
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 facilities installed in UK Universities.
There are 47 data files in this dataset.

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 data is from tests performed at The University of Manchester on a commercial 240 kW 180 kWh lithium-ion battery electrical energy storage system (BESS) within the EPSRC funded Multi-scale ANalysis for Facilities for Energy STorage (Manifest) Project (EP/N032888/1). The system is connected to the LV network (400 V) and is located on campus within a mixed-use (offices, lecture theatres, and laboratories) university building (so behind the meter). The tests carried out in March 2017 are described in the PDF Experiment_Description_Readme.pdf

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.

The Ecosystem Land Use Modelling and Soil Carbon Flux Trial (ELUM) measured greenhouse gas and soil carbon changes arising as a result of direct land-use change to bioenergy crops.

Ecosystem Land Use Modelling and Soil Carbon Flux Trial (ELUM) was funded and commissioned by the Energy Technologies Institute (the ETI) and carried out by the Centre for Ecology and Hydrology (CEH), the University of Aberdeen, the University of Southampton, Forest Research, Aberystwyth University, the University of Edinburgh and the University of York.

The ELUM project aims were to provide greater understanding on the greenhouse gas (GHG) and soil carbon changes arising as a result of direct land-use change (dLUC) to bioenergy crops, with a primary focus on the second-generation bioenergy crops Miscanthus, short rotation coppice (SRC) willow and short rotation forestry (SRF).

Experimental fieldwork on Miscanthus, SRC Willow, and SRF, and adjacent control sites, was conducted at locations across the length and breadth of mainland UK to capture data across both temporal and spatial scales.

Half-hourly electricity daily load profiles for standard UK profile class definitions (01 Domestic Unrestricted; 02 Domestic Economy 7; 03 Non-domestic Unrestricted; 04 Non-domestic Economy 7; 05 Non-domestic Maximum Demand 0-20% Load Factor; 06 Non-domestic Maximum Demand 20-30% Load Factor; 07 Non-domestic Maximum Demand 30-40% Load Factor; 08 Non-domestic Maximum Demand >40% Load Factor) for weekday (Wd), Saturday (Sat), and Sunday (Sun) at five different times of year (Autumn Aut, Winter Wtr, Spring Spr, High Summer HSr, Summer Smr) The year is broken down into the following seasons: (a) Spring: defined as the period from the day of clock change from GMT to BST in March, up to and including the Friday preceding the start of the summer period; (b) Summer: defined as the ten-week period, preceding High Summer, starting on the sixteenth Saturday before the August Bank Holiday; (c) High Summer: defined as the period of six weeks and two days from the sixth Saturday before August Bank Holiday up to and including the Sunday following August Bank Holiday; (d) Autumn: defined as the period from the Monday following the August Bank Holiday, up to and including the day preceding the clock change from BST to GMT in October; (e) Winter: defined as the period from the day of clock change from British Summer Time (BST) to Greenwich Mean Time (GMT) in October, up to and including the day preceding the clock change from GMT to BST in March.
For creation and first use of the data see: Load Profiles and their use in Electricity Settlement (Load_Profiles.pdf)

The data were gathered through a survey undertaken as part of the Financing Community Energy research project with the aim of improving understanding of the business models and financial characteristics of community energy projects in the UK. Data from individual projects have been aggregated into multi-project records in accordance with confidentiality undertakings made to survey participants. The survey formed part of the Financing Community Energy research project, itself part of the UK Energy Research Centre research programme. An analysis of the data has been published in Nature Energy (February 2020) https://doi.org/10.1038/s41560-019-0546-4.
This project was led by Professor Carly McLachlan of the Tyndall Centre for Climate Change Research, University of Manchester, and funded as part of the UKERC research programme. It involved researchers from the University of Manchester, University of Strathclyde, and Imperial College. The researchers involved with the survey were Carly McLachlan, Sarah Mander, Maria Sharmina, Ed Manderson and Tim Braunholtz-Speight (University of Manchester); Matthew Hannon (University of Strathclyde); and Jeff Hardy (Imperial College). Christina Birch and Christopher Walsh (University of Manchester) also provided some research assistance to the survey. Some data from Community Energy Englands (CEE) 2017 State of the Sector survey was also used, by agreement with CEE.
The survey covered characteristics of community energy organisations, and of the projects they run. With regard to organisations, it included legal structure, annual turnover, numbers of paid staff and volunteers, and numbers of members. In relation to each project, topics included: energy activities (including electricity or heat generation, and energy efficiency); ownership (sole or partnership type); financing (details of each instrument type, value, terms etc.); resources employed (including sites, technical, financial and legal services, general administration); costs (operating and financing); revenues (values and sources); value propositions (a range of economic, social and environmental propositions); customers (types, rates paid, etc.); and other beneficiaries.

Annual total installed capacity of wind energy (MW) from 1990 to end of 2009 compiled from 5 separate (but not necessarily independent) sources. Sourced from : European Wind Energy Association 2002; Global Wind Energy Council 2005; BP Statistical Review of World Energy 2006/2007; Windpower Monthly, April 2008; GWEC - Global Wind 2009; Global Wind Energy Council, March 2010
Version containing records up to 2009 GlobalWindCapacity.csv
Previous version (up to 2007) GlobalWindCapacity_v2008.csv

DS4DS: Heat, non-heating electricity and home-based transport energy demand for Great Britain in 2009 at 1 km square resolution in units of kWh/km² from UK Department of Energy & Climate Change (DECC) sub-national energy consumption statistics.
Please acknowledge the School of Civil and Building Engineering, Loughborough University when making any use of this data. Data is available as a txt file (which is really a csv file) and ESRI Shapefiles.
Methods and results were reported in Taylor S, Firth S, Wang C, Allinson D, Quddus M, Smith P (2014) Spatial mapping of building energy demand in Great Britain. Global Change Biology Bioenergy, 123-135. https://doi.org/10.1111/gcbb.12165.
The coordinate system is British National Grid.

DS4DS Great Britain energy demand (2009) - Proportions of energy demand projected for future years (for application to table of energy demand data for GB in 2009).
Tables of dimensionless scenario factors for 2000 to 2050 in 5-year steps for domestic and non-domestic heat and non-heating electricity demand for two scenarios: UKERC scenarios ADD and LC
Please acknowledge the School of Civil and Building Engineering, Loughborough University when making any use of this data. Two csv files, one for each scenario.
Methods and results were reported in Taylor S, Firth S, Wang C, Allinson D, Quddus M, Smith P (2014) Spatial mapping of building energy demand in Great Britain. Global Change Biology Bioenergy, 123-135.

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 Aquifer Brine project assessed the modeled impact of removing brine from potential undersea carbon dioxide stores on store capacity and costs using selected real locations and synthetic models. It followed on from the UK Storage Appraisal Project which assessed the UK CO2 storage capacity for CCS in offshore geological formations.

The Aquifer Brine project assesses the potential for brine production through dedicated wells in target CO2 storage formationsto increase CO2 storage capacity and reduce the overall cost of storage - as well as any other potential benefits forCO2 store operators associated with brine production.

It followed on from the UK Storage Appraisal Project.

Proprietary software is needed to access the geological models. See ReadMe file with data for details.

Database of baseline energy use and emissions for key process industries (iron & steel; chemicals; food & drink manufacture; paper; cement) in the UK industrial sector and opportunities for reducing these (improvement potentials). Total sectoral energy flows (PJ) and emissions (MtCO₂e) and sectoral energy flows (PJ/t) and emissions (kgCO₂e/t) per unit output.
Industrial sectors: Iron & steel; Chemicals; Food & drink manufacture; Paper; Cement The usable energy database (UED) contains baseline energy use and emissions, and opportunities for reducing these (improvement potentials). The project report should be read alongside the UED. The work is currently presented as a Beta version to allow feedback from project partners and other interested parties to be incorporated in a final version. The UED was produced to improve the representation of the UK industrial sector within the whole systems TIMES model developed by the UK Energy Research Centre (UKERC). Due to the substantial variability in energy use within the industrial sector the approach was to undertake bottom-up studies of subsectors of industry that were important from an energy use and emissions perspective, and to supplement this with information on cross-cutting technologies that could be applied throughout industry. Baseline data variously sourced from: International Steel Statistics Bureau (ISSB); the Confederation of Paper Industries; Mineral Products Association; and others (for a full list of source references see the project report.)
Please acknowledge the UKERC Energy Demand theme when making use of this data
The data is presented as .xls and .xlsx
The final project report is UED_Documentation.pdf

Levelised unit cost data (£/MWh 2006) for electricity generation from various primary energy sources in various countries. Unit cost“relevance” rating. Plant types comprise: Coal, Nuclear, Gas, Wind, Coal (other), CHP, PV, Gas (other), Biomass & Energy Crops, Hydro, Waste, Wind (offshore), Geothermal, Solar Thermal, Wave & Tidal Stream, Fossil Fuel (other), Fuel cell, Nuclear (other). The database is in Heptonstall2007_LevelisedElectricityCostData.xls
Contains information for Australia, Austria, Belgium, Brazil, Canada, China, Czech Republic, Denmark, Finland, France, Germany, Greece, India, Indonesia, Italy, Japan, Mexico, Netherlands, New Zealand, Portugal, Spain, Romania, Slovakia, South Africa, South Korea, Sweden, Switzerland, Turkey, UK, USA
A full list of references with links to original documents is provided in "A Review of Electricity Unit Cost Estimates" (PDF 300 KB)

The Local Engagement in UK Energy Systems (LEUKES) Database is about local authority energy projects. The data being made available is a database of 458 UK local authority energy projects collated from 29 different data sources. The energy projects database was produced as part of the Local Engagement in UK Energy Systems (LEUKES) project. The database is developed from datasets published by the European Commission, UK and devolved Governments, and their agencies which included information on grants and loans which LAs use for financing energy initiatives and information on operational energy projects.
The research aimed to examine the current and future contribution of local governments to changing energy production, supply and use with a primary focus on providing knowledge about the extent of local engagement in energy initiatives under current institutional structures; and, identifying local energy governance and business models being developed. This data formed one component of research in the LEUKES project and was funded by the Research Councils UK Energy Programme as part of the UK Energy Research Centre (UKERC) research programme and the Energy Technologies Institute (the ETI) and was carried out by the University of Edinburgh. https://heatandthecity.org.uk/project/local-engagement-with-uk-energy-systems/

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 and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

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 and at www.thamesvalleyvision.co.uk.
Data format information is in EMMA_readme.txt.
Use of these data is subject to terms and conditions.

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 and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

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 and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

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 and at www.thamesvalleyvision.co.uk.
Use of these data is subject to terms and conditions.

Spatially disaggregate half-hourly data on GB electricity generation and consumption, including detailed per-generator half-hourly generation and balancing operations. The P114 data will be uploaded in batches currently the dataset contains folders for data released in June 2016, January 2018, January 2019, January 2020, January 2021, January 2022, January 2023 and April 2023. A data description document or read me file is available for each release. NB the number of records is very large - indicated by the value of 999M in the number of records field.

The datasets available for download include:1. The thermal characteristics for 16 dwelling categories for all the LSOAs in England and Wales before and after considering energy efficiency measures.2. The magnitude and duration of positive and negative flexibility services that can be provided by air source heat pumps in dwellings at local authority level. Dates refer to the input data to the model.

Annual residential heat demand data for Scotland at Lower Layer Super Output Area (LSOA) level, before and after energy efficiency measures

This data set presents the underlying data used to produce the results for the UKERC working paper: Sensitivity Analysis of Net Zero Pathways for UK Industry or by searching the UKERC Publications section of this website. These data are the outputs of a how the The Net-Zero Industry Pathways (N-ZIP) model results are affected by changing a wider range of inputs than have been previously studied. The Net-Zero Industry Pathways (N-ZIP) model, developed by Element Energy, has been used by both the Climate Change Committee (CCC) and the Government to explore how industry can be decarbonised in way that is consistent with the UKs netzero greenhouse gas (GHG) target. This data set presents the underlying data used to produce the results for the UKERC working paper: Sensitivity Analysis of Net Zero Pathways for UK Industry or by searching the UKERC Publications section of this website.

Annual heat demand data for England and Wales at Lower Layer Super Output Area (LSOA) level, before and after energy efficiency measures. Normalised half-hourly profiles for heat production and energy consumption of different heating technologies. Costs for energy efficiency measures by local authority. The three datasets available for download include:

1. Annual heat demand data before and after adopting energy efficiency measures for 16 dwelling categories for all the LSOAs in England and Wales.
2. Normalised half-hourly profiles for the heat production and energy consumption of air-source heat pumps, ground source heat pumps, gas boilers and resistance heaters based on the UK average daily outside air temperature profile from 2013
3. The estimated costs to implement the energy efficiency measures to reach the annual heat demand displayed in the annual heat demand dataset, for each local authority in England and Wales.

This data set was updated on 14/10/2021. The top-level folder holds the latest version and a README file detailing the changes. The original version is retained in a sub-folder.

Geological and reservoir engineering models for five potential CO2 storage sites around the UK. Five sites were selected from the UKs national CO2 storage database CO2Stored which was created by the UK Storage Appraisal Project (UKSAP). Outline storage development plans and budgets were prepared for each.
The project was funded by DECC, commissioned by the ETI and carried out by Pale Blue Dot Energy, Axis Well Technology and Costain.
The data are further described by read me files in each directory and by reports in the Reports directory.

Modelling of the costs, efficiencies and greenhouse gas emissions of biomass supply chains with and without significant pre-processing.

The TEAB project compares the costs, efficiencies and GHG emissions of biomass supply chains with and without significant pre-processing, to assess whether and how pre-processing steps can benefit UK bioenergy supply chains.

Ten supply chains were selected for modelling and analysis in the project, two of which generate heat, and eight generating power. These are compared in groups according to their shared conversion technology, and all the chains are able to use a blend of Miscanthus and woody feedstocks (from 0-100%).

Available here are gPROMS and Excel models describing bioenergy supply chains, and project reports. Further README files in the models and reports sub-directories describe the contents further.

Data arising from multiple measurement campaigns comprising primarily velocimetry conducted at the European Marine Energy Centre (EMEC) Tidal Test Site during the ReDAPT Tidal Project. Data arising from multiple measurement campaigns comprising primarily velocimetry conducted at the European Marine Energy Centre (EMEC) Tidal Test Site during the ReDAPT Tidal Project. These campaigns consisted of both stand-alone seabed mounted Acoustic Doppler Profiling and Tidal Energy Converter (TEC) mounted Acoustic Doppler Profiling. Multiple instrument configurations were used across multiple seasons between June 2011 and October 2014. The data was used for resource characterisation and the validation of multiple numerical modelling techniques used in the Tidal Industry. Data is provided in MATLAB v7.3 .mat files and has also been converted to NETCDF. There is a project www at <a href="http://www.eti.co.uk/programmes/marine/redapt">http://www.eti.co.uk/programmes/marine/redapt</a>. Further information is also available at <a href="http://redapt.eng.ed.ac.uk/index.php">http://redapt.eng.ed.ac.uk/index.php</a>

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

The Electricity Pool operated from 1st April 1990 to 26th March 2001, controlling the trading of electricity within England and Wales. The Electricity Pool data contains system demand and transmission losses plus the price information (/MWh) within the trading pool, comprising the three main prices: Pool purchase price (PPP), the basis of payments to generators; Pool selling price (PSP), the basis of payments by suppliers; and system marginal price (SMP), the highest offered price for any generating set scheduled by the Pool to run before system constraints are taken into account. The Electricity Pool system was replaced by the New Electricity Trading Arrangements (NETA/BETTA) from 27 March 2001. (Note : It is mandatory to acknowledge the UKERC EDC and the data owner Poolit Ltd when any use of this data is made in publications) Explanation of parameters and pool operation
PSP : Pool Selling Price, is the price paid mostly by RECs purchasing electricity from the Pool to sell to their final commercial, industrial and residential customers; this value is determined for two distinct price-rule regimes referred to as Table A and Table B periods, such that: PSP = SMP + CC + UPLIFT = PPP + UPLIFT
SMP : System Marginal Price is the highest offered price for any generating set scheduled by the Pool before system constraints are taken into account
LOLP : Loss Of Load Probability, a decreasing function of the expected amount of excess capacity available during each half-hour period, determined for each half-hour as the probability of a supply interruption due to generation capacity being insufficient to meet demand (the greater the amount of capacity available relative to expected demand in any half-hour, the lower the LOLP and therefore the lower the capacity charge per KWH paid to generators)
VOLL : Value Of Lost Load, representing the per kWh willingness of customers to pay to avoid supply interruptions; it was set by the Director General of the Office of Electricity Regulation (OFFER) at 2,000 per megawatt-hour (MWH) for 1990/91 and was then increased annually by the growth in the RPI
PPP : Pool Purchase Price, is the price paid to generators for electricity purchased into the Pool
CC : Capacity Charge: CC = LOLP * (VOLL - SMP) is a signal to generators of the necessity for additional generation capacity and to consumers that their consumption has a significant probability of requiring the maximum amount of generating capacity available in that load period
UPLIFT : a charge to compensate generators for reserve, plant available but not actually used to meet demand, and startup costs, known only ex post and therefore the only price uncertainty from the day ahead perspective; it is collected over at least 28 Table A pricing periods each day (UPLIFT is zero for Table B pricing periods) according to the formula: Availability Payment/MWh = LOLP*(VOLL - max(SMP, bid price))

Model output from UK MARKAL Elastic Demand for all UKERC Energy 2050 project revised scenarios.
This consists of datasets as .xls files plus graphics summaries as PDFs Please acknowledge the UKERC Energy Systems Theme when making use of this data.
Reference this data source as: Keppo I Skea J. and Ekins P. 2013 UK Energy 2050 Revised Scenario Set Data UKERC Energy Data Centre http://ukedc.rl.ac.uk
The revisions are described in UKERC_Energy_2050_RevisedScenarios.pdf
There is further information at ReadMe_2050Scenarios.htmll
The .xls files are in the Data subdirectory

Historic and projected UK household and population numbers in thousands Three datasets:
UK_households.csv Number of households per year in thousands
UK_population.csv UK population in thousands
UK_population_households.xls Both combined plus calculated number of persons per household
Data developed by the Defra MTP from ODPM (now DCLG) figures, interpolated between 1991 and 2001 to account for 2001 Census data. Population projections in the period 2003 to 2021 have been linearly interpolated from five-years interval projections by UK government. For further information see BNXS25 UK household and population figures, 1970-2020, Version 2.1 (updated 12/1/2007)

Technology variants, capital & O&M costs, efficiency, plant life-time characteristics for electricity generating plant as input to the UKERC Markal Energy Model (2007) sub-divided as CHP, Renewables, Nuclear, CO2 Pipeline & Storage, Gas-fired Plant, Coal, Electricity data for MARKAL The MARKAL_Electricity_cost_data_version_EDC_2007.xls spreadsheet contains cost information in the electricity generation sector drawn from a variety of sources. These references are listed in the worksheet entitled Sources.
The workbook is divided into labelled worksheets, according to different generic types of generating technology: CHP, Renewables, Nuclear, CO₂ pipeline and storage, Gas-fired plant, Coal. Each worksheet contains the data as input to the MARKAL model, showing a range of aspects of different generation technologies. The full set of MARKAL data covering all technologies is included in worksheet 7, entitled 7 Electricity data for MARKAL.
In worksheets 1-6, beneath the MARKAL data, other data has been added from the sources reviewed to illustrate the range of data which the models assumptions could plausibly have covered. The data is provided in two forms. In the first worksheet for each technology, the data is included in the form in which it is presented in the original sources. Thus, the units and currencies are not always those of the MARKAL model. Notes have been inserted to indicate the currency or unit of measurement. The information has been provided in this way so as to maintain transparency. In the second worksheet for each technology (marked A - for Adjusted), the data has been adjusted to match the currencies and units used in MARKAL. The numbers which have been subject to adjustment have been highlighted in yellow. A separate sheet showing the conversion factors used has been included (Conversion).
The information from other sources covers only a subset of the information within MARKAL. The definitions of key terms (such as operational and maintenance cost), is rarely consistent from one source to the next. In two places therefore, new columns have been inserted to the original structure. Information on construction periods (in years) has been included adjacent to the existing column showing first year available. The lack of distinction in the sources between fixed and variable O&M costs has also resulted in the creation of a single O&M column so that information can still be included.
The workbook should be used in conjunction with the report: Derek Smith, Comparative Cost Information: Supporting the development of the MARKAL MACRO model of the UK Energy Research Centre (UKERC), November 2006 which summarises the cost comparisons. These summaries include additional information to provide the context from which the data in this workbook have been drawn.

The ETI-funded UKSAP (UK Storage Appraisal Project) provides a fully auditable and defensible overall estimate of UK CO2 storage capacity for CCS in offshore geological formations. <p>The whole UKSAP results have been made available in the CO2 Stored database.</p><p>Most storage reservoirs were modeled as simplified representative structures. Detailed reservoir simulation models known as Exemplar Models were created for two sites, Bunter and Forties. Both were modeled with proprietary geological simulation software Eclipse and Petrel and these models are made available here.</p>

Model output (from UK MARKAL Elastic Demand) for all UKERC Energy 2050 project scenarios. Please acknowledge the UKERC Energy Systems Theme when making use of this data.
Reference this data source as: Strachan N. Anandarajah G. Hughes N. and Ekins P. 2010 UKERC Energy 2050 energy systems scenario data UKERC Energy Data Centre http://ukedc.rl.ac.uk
More information is available in UKERC_Energy_2050_Scenario_Data.pdf and ReadMe_2050Scenarios.html

This dataset relates to the modelling component of the UKERC Phase 4 Project '4.2a Investment in infrastructure'. The aim is to assess the scale of investment risks in the electricity generation sector associated with uncertainty over the pathway towards net zero. The model comprises three components:1. Input data. Based in Excel, contains the scenario assumptions on the electricity system including generation technologies, wind and solar power availability, demand profiles, interconnector and storage capacities etc.2. Modelling files based on the open-source Antares modelling framework. This requires additional free software to be able to run the model. The model calculates the hourly dispatch of all types of plant included in the system.3. Excel-based financial model, based on post-processing the output of the Antares model.Additional 1_Metadata.xls, Input_ReadMe and Outputs_ReadMe files explain the method for running each step of the modelling process. Software available at https://antares-simulator.org/. Version used 8.0.2

These files are the core subset of the updated and expanded versions of the data originally collated for the UKERC "Costs and Impacts of Intermittency - 2016 update" project, see https://ukerc.ac.uk/project/the-intermittency-report/. This dataset includes the references, costs and impacts from this systematic review of the evidence on the costs and impacts of intermittent electricity generation technologies. These files are the core subset of the updated and expanded versions of the data originally collated for the UKERC "Costs and Impacts of Intermittency - 2016 update" project, see https://ukerc.ac.uk/project/the-intermittency-report/.
The dataset was further updated and expanded for a 2020 paper in Nature Energy "A systematic review of the costs and impacts of integrating variable renewables into power grids" - https://doi.org/10.1038/s41560-020-00695-4

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.