Abstract copyright UK Data Service and data collection copyright owner.

During 1998, in line with other European Union (EU) member states, the UK adopted the new European System of Accounts (ESA 95), to be used by the Office for National Statistics (ONS) for the production of financial data. In September 1998 the UK National Accounts were published for the first time using ESA95. Under this system, ONS is required to compile more detailed regional economic information than was previously the case and now produces regional Gross Value Added (GVA - formerly Gross Domestic Product or GDP, to which some series still refer), regional Household Accounts and regional Gross Fixed Capital Formation. Most Regional Accounts data are now consistent with ESA95 - users should refer to the information in the 'Main Topics' section below, and the documentation.
Regional Accounts data are compiled mainly from the UK National Accounts (Blue Book), the New Earnings Survey (NES) and the Annual Business Inquiry (ABI 2).

For the 8th edition (December 2002), data files covering Gross Value Added (GVA - formerly GDP) by GOR, Industry, Manufacturing Class and other tables were added to the dataset. On 10th December 2002, however, ONS withdrew the 8th edition GVA data files, due to errors discovered in the data.


Main Topics:

PART I: Regional Gross Value Added (GVA - formerly GDP) up to 1998/1999 - European System of Accounts 1995 (ESA95) basis.
Files:
1. GVA by Government Office Region (GOR) 1989-1999: file gvagor8999 (withdrawn - see 'Abstract' section for details).
2. GVA by industry 1989-1999: file gvaind8999 (withdrawn).
3. GVA by manufacturing class 1989-1999: file gvamanclass8999 (withdrawn).
4. GVA 1989-1999 (various tables): file gva8999 (withdrawn).
5. Compensation of Employees (COE) by industry 1989-1998: file indcomp8998.
6. Compensation of Employees (COE) by manufacturing class 1989-1998: file compcls8998.
Due to the withdrawal of GVA data, the following GDP data are also available, but users should bear in mind the age of these data when conducting analysis:
1. GDP by GOR 1989-1999: file gpdgor8999.
2. GDP by Industry 1989-1998: file gdpind8998.
3. GDP by Manufacturing Class 1989-1998: file gpdmancls8998.

PART II: ESA95 Regional household (total and disposable) income up to 1999, consumption expenditure up to 1999.
Files:
1. Regional household income (total and disposable) basic breakdown of income type 1989-1999 by GOR: file hhacc8999.
2. Individual Consumption Expenditure by GOR 1994-1999: file ice32gor9499.

There is no longer a PART III.

PART IV: Regional GDP, by factor of income and by industry, up to 1996; county GDP and GDFCF up to 1995.
Files
1. GDP by factor income by SSR 1971-1996 and by GOR 1984-1996: files gdpssr7196 and gdpgor8496.
2. GDP by county for selected years between 1977-1996: file gdpcty7796.
3. GDP by industry by SSR 1982-1996: file gdpind8296. Users should note that ONS have advised the UK Data Archive that the data in this file are no longer published by ONS, and are useful for historic purposes only.
4. GDP by manufacturing class by SSR 1982-1995: file gdpmancls8295 - again these data are no longer published by ONS, and are of historic use only.
5. Income from Employment (IFE) by industry by SSR 1982-1996: file ifeind.
6. Income from employment by manufacturing class by SSR 1982-1995: file ifecls.

PART V: ESA79 Regional household and personal (total and disposable) income and consumers' expenditure up to 1996.
Files:
1. Household income (total and disposable by GOR 1984-1996 and by SSR 1984-1996: files hhsougor and hhsoussr.
2. Household income (total and disposable) by county 1984-1995: file hhinc.
3. Personal income (total and disposable) by GOR 1984-1996 and by SSR 1971-1996: files perincg and perincs.xls
4. Consumers' Expenditure by SSR 1971-1996 and by GOR 1994-1996: files cons32 and consgor.

PART VI: Local area gross domestic product.
1. Figures for 1993-1998 on new NUTS regional basis: file gdpn23.
2. Estimates for old administrative counties of England (first made available June 2001): file admcty.

File regsublochhd contains regional, sub-regional and Local Area Household Accounts, and includes the following tables:
Table 1: Total Household Income1 by Region (NUTS1) 1995-1999.
Table 2: Gross Disposable Household Income by Region (NUTS1) 1995-1999.
Table 3: Total Household Income by NUTS 1 & 2 Areas.
Table 4: Gross Disposable Household Income by NUTS 1 & 2 Areas.
Table 5: Gross Disposable Household Income - Components, NUTS 1 & 2: 1995.
Table 6: Gross Disposable Household Income1 - Components, NUTS3.
Table 7: Total Household Income1 - Components, NUTS3.

Abstract

The 66th round (July 2009-June 2010) of NSS is earmarked for survey on 'Household Consumer Expenditure' and 'Employment and Unemployment'. The survey on 'household consumer expenditure' is the eighth quinquennial survey in the series, the last one being conducted in the 61st round (2004-2005) of NSS. The period of survey was one year from 1st July 2009 to 30th June 2010. The survey period of this round divided into four sub-rounds of three months' duration each as follows:

sub-round 1 : July - September 2009
sub-round 2 : October - December 2009 sub-round 3 : January - March 2010
sub-round 4 : April - June 2010

In each of these four sub-rounds equal number of sample villages/ blocks (FSUs) allotted for survey with a view to ensuring uniform spread of sample FSUs over the entire survey period.

Household Consumer Expenditure The programme of quinquennial surveys on consumer expenditure and employment & unemployment has been adopted by the National Sample Survey Office (NSSO) since 1972-73. Under the programme, the survey on consumer expenditure provides a time series of household consumer expenditure data, which is the prime source of statistical indicators of level of living, social consumption and well-being, and the inequalities thereof. Apart from the quinquennial series (QS), there also exists an “annual series”, comprising consumer expenditure surveys conducted in the intervening periods between QS rounds - starting from the 42nd round (July 1986 - June 1987) and using a smaller sample.

Household consumer expenditure (HCE) during a specified period, called the reference period, may be defined as the total of the following: (a) expenditure incurred by households on consumption goods and services during the reference period (b) imputed value of goods and services produced as outputs of household (proprietary or partnership) enterprises owned by households and used by their members themselves during the reference period (c) imputed value of goods and services received by households as remuneration in kind during the reference period (d) imputed value of goods and services received by households through social transfers in kind received from government units or non-profit institutions serving households (NPISHs) and used by households during the reference period. Reference period and schedule type: The reference period is the period of time to which the information collected relates. In NSS surveys, the reference period often varies from item to item. Data collected with different reference periods are known to exhibit certain systematic differences. In this round, two schedule types have been drawn up to study these differences in detail. Sample households will be divided into two sets - Schedule Type 1 will be canvassed in one set and Schedule Type 2 in the other. The reference periods to be used for different groups of consumption items are given below, separately for each schedule type.

Geographic coverage

The survey covered the whole of the Indian Union except (i) interior villages of Nagaland situated beyond five kilometres of the bus route and (ii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year. For Leh (Ladakh) and Kargil districts of Jammu & Kashmir there will be no separate sample first-stage units (FSUs) for 'central sample'. For these two districts, sample FSUs drawn as 'state sample' also treated as central sample.

Analysis unit

Households and Persons

Universe

Households and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample design Outline of sample design: A stratified multi-stage design was adopted for the 66th round survey. The first stage units (FSUs) were the 2001 Census villages (Panchayat wards in case of Kerala) in the rural sector and Urban Frame Survey (UFS) blocks in the urban sector. In addition, two non-UFS towns of Leh and Kargil of Jammu & Kashmir were also treated as FSUs in the urban sector. The ultimate stage units (USU) were households in both the sectors. In case of large FSUs, one intermediate stage of sampling was the selection of two hamlet-groups (hgs)/subblocks (sbs) from each rural/urban FSU.

Sampling frame for first stage units: For the rural sector, the list of 2001 Census villages (henceforth the term 'village' will mean Panchayat wards for Kerala) constituted the sampling frame. For the urban sector, the list of latest available UFS blocks was considered as the sampling frame. For non-UFS towns, the frame consisted of the individual towns (only two towns, viz., Leh & Kargil constituted this frame).

Stratification: Within each district of a State/UT, generally speaking, two basic strata were formed: i) rural stratum comprising all rural areas of the district and (ii) urban stratum comprising all urban areas of the district. However, within the urban areas of a district, wherever there were one or more towns with population 10 lakhs or more as per Census 2001 in a district, each of these formed a separate basic stratum and the remaining urban areas of the district were considered as another basic stratum. Sub-stratification: Each rural stratum was divided into 2 sub-strata as follows: sub-stratum 1: all villages with proportion of child workers (p) >2P (where P is the average proportion of child workers for the State/UT as per Census 2001) sub-stratum 2: remaining villages

Total sample size (FSUs): At all-India level, 12784 FSUs were allocated to the Central sample and 15132 FSUs to the State sample. Further, the data of 24 State sample FSUs of Leh and Kargil districts of J&K surveyed by DES, J&K, were included in the Central sample.

Allocation of total sample to States and UTs: The total number of sample FSUs was allocated to the States and UTs in proportion to population as per Census 2001 subject to a minimum sample allocation to each State/UT, and subject to resource availability in terms of field investigators.

Allocation of State/UT level sample to rural and urban sectors: State/UT level sample size was allocated between the two sectors in proportion to population as per Census 2001 with double weightage to urban sector, subject to the restriction that the urban sample size for bigger States like Maharashtra, Tamil Nadu, etc. should not exceed the rural sample size. A minimum of 16 FSUs (to the extent possible) was allocated to each State/UT separately for rural and urban areas. Further, the State-level allocations for both rural and urban sectors were adjusted marginally in a few cases to ensure that each stratum/sub-stratum got a minimum allocation of 4 FSUs.

Allocation to strata/sub-strata: Within each sector of a State/UT, the sample size was allocated to different strata/sub-strata in proportion to population as per Census 2001. Allocations at stratum/sub-stratum level were adjusted to multiples of 4 with a minimum sample size of 4 and equal-sized samples were allocated to the four sub-rounds.

Selection of FSUs: For the rural sector, from each stratum/sub-stratum, the required numbers of sample villages were selected by probability proportional to size with replacement (PPSWR), size being the population of the village as per Census 2001. For the urban sector, FSUs were selected from each stratum using Simple Random Sampling Without Replacement (SRSWOR). Both rural and urban samples were drawn in the form of two independent sub-samples.

Formation and selection of hamlet-groups/sub-blocks: Selected FSUs with approximate population 1200 or more were divided into a suitable number of geographically compact 'hamlet-groups' (having more or less equal population) in the rural sector and 'sub-blocks' in the urban sector .

Selection of hamlet-groups/sub-blocks: Hamlet-groups (hg)/sub-blocks (sb) were selected from FSUs where hamlet-groups/sub-blocks were formed, two in the following manner. The hg/sb with maximum percentage share of population was always selected and termed hg/sb 1;one more hg/sb was selected from the remaining hg’s/sb’s by simple random sampling (SRS) and termed hg/sb 2. Listing and selection of the households was done independently in the two selected hamlet-groups/sub-blocks.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household consumer expenditure is measured as the expenditure incurred by a household on domestic account during a specified period, called reference period. It includes the imputed values of goods and services, which are not purchased but procured otherwise for consumption. In other words, it is the sum total of monetary values of all the items (i.e. goods and services) consumed by the household on domestic account during the reference period. The imputed rent of owner-occupied houses is excluded from consumption expenditure. Any expenditure incurred towards the productive enterprises of the households is also excluded from household consumer expenditure.
To make the definition of household consumption operational, clear guidelines are needed not only on what is included in household consumer expenditure and what is excluded, but also on (a) the identification of the household performing each act of consumption (b) the assigning of a time to of each act of consumption. Only then can one attempt to record the consumption of a household H within a reference period P. It has been found convenient to assign different meanings of the word “consumption” (and hence different approaches to its measurement) for different categories of consumption items.

GDP is the value of goods and services produced within a county: consumption, investment, government spending, and net exports. This map shows which of the two components of private-sector GDP, goods-producing industries and service-providing industries, is predominant.Goods-Producing industries:GDP from North American Industry Classification System (NAICS) 11, 21, 23, 31-33: agriculture, forestry, fishing, and hunting; mining, quarrying, and oil and gas extraction; construction; and manufacturing.Service-Providing industries:GDP from North American Industry Classification System (NAICS) 22, 42, 44-45, 48-49, 51, 52, 53, 54, 55, 56, 61, 62, 71, 72, 81: utilities; wholesale trade; retail trade; transportation and warehousing, excluding Postal Service; information; finance and insurance; real estate, rental, and leasing; professional, scientific, and technical services; management of companies; administrative and support and waste management and remediation services; educational services; health care and social assistance; arts, entertainment, and recreation; accommodation and food services; and other services (except government and government enterprises).Data from Bureau of Economic Analysis. Table CAGDP2, downloaded ‎February ‎2, ‎2021.https://www.bea.gov/data/gdp/gdp-county-metro-and-other-areas , feature layer linked below:

Abstract

The Poverty, Income, Consumption, and Expenditure Survey 2017 is the main data source for the compilation of the informal sector, living conditions, poverty levels, and weights for the Consumer Price Index (CPI). The survey is based on a sample of 32,256 households, representative at Province and District Levels.

The objectives of the survey are to: - Estimate private consumption expenditure and disposable income of the household sector - Compile the production account of the agricultural sector - Study income/expenditure disparities among socio-economic groups - Estimate the contribution of the informal sector to GDP in Zimbabwe - Estimate the size of household transfer incomes within and outside the country - Calculate weights for the Consumer Price Index (CPI) - Calculate the poverty line, measure the poverty rate and inequality - Provide data useful to formulate national policies for social welfare programmes - Obtain data for poverty mapping - Obtain data useful in measuring the demographic dividend for Zimbabwe

Geographic coverage

• National Coverage: 62 administrative districts of Zimbabwe
• Rural and Urban areas
• Land-use sectors: Communal Lands (CL), Small Scale Commercial Farms (SSCF), Large Scale Commercial Farms (LSCF), Resettlement Areas (includes Old Resettlement Areas (ORA), A1 Farms and A2 Farms), Urban Council Areas (UCA), Administrative Centres (AC), and Growth Point (GP) and Other Urban Areas (OUA), e.g. Services Center and Mines.

Analysis unit

• Individuals
• Households

Universe

The sample is representative of the whole population of Zimbabwe living in private households. The population living in collective households or in institutions such as military barracks, prisons and hospitals are excluded from the sampling frame.

Kind of data

Sample survey data [ssd]

Sampling procedure

At the first sampling stage, the sample EAs for the PICES 2017 are selected within each stratum (administrative district) using random systematic sampling with Probability Proportional to Size (PPS) from the ordered list of EAs in the sampling frame. The measure of size for each EA are based on the total number of households identified in the 2012 Population Census sampling frame. The EAs within each district are ordered first by rural and urban codes, land-use sector, ward and EA number. This provides implicit land-use and geographic stratification of the sampling frame within each district, and ensures a proportional allocation of the sample to the urban and rural areas of each district.The Complex Samples module of the SAS software is used for selecting the sample EAs systematically with PPS within each stratum at the first stage. The module uses the “SURVEY SELECT” sampling procedure.

At the second sampling stage, a random systematic sample of 14 households are selected with equal probability from the listing of each sample EA. Reserve households are selected for replacements. The reason why the replacement of non interview households are considered was to maintain the effective sample size and enumerator workload in each sample EA. Four households are selected for possible replacement, and thus a total of 18 households are selected from each sample EA. A systematic subsample of 4 households are then selected from the 18 households, and the remaining 14 sample households are considered the original sample for the survey. A spreadsheet is developed for selecting the 14 sample households and 4 reserve households for possible replacement in each sample EA. This spreadsheet includes items for the identification of the sample EA, and formulas for the systematic selection of households once the total number of households listed has been entered.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The PICES 2017 data entry is conducted by the ZIMSTAT Data Entry Unit using the CSPro software to enter the data. Data entry was done from January 2018 to June 2018. Data is captured twice by different people for purposes of verification. Data from the daily record books (the household food consumption diaries) have been entered from July to November 2018. SAS and STATA software is used for data processing. Data cleaning is done at all stages i.e. during data entry and data processing to check for the consistency of the data. Tables are then generated for use in report writing.

Response rate

Out of a total of 32,256 sampled households, a total of 31,195 households successfully completed interviews. This gives a response rate of 96.7 percent of the sampled households.

Sampling error estimates

The standard error, or square root of the variance, is used to measure the sampling error, although it may also include a small variable part of the non-sampling error. The variance estimator should take into account the different aspects of the sample design, such as the stratification and clustering. Programs available for calculating the variances for survey data from stratified multi stage sample designs such as the PICES 2017 include STATA and the Complex Samples module of SPSS as well as SAS and Wesvar. All these software packages use an ultimate cluster (linearized Taylor series) variance estimator. The Complex Samples module of STATA is used with the PICES 2017 data to produce the sampling errors.

Australia Household Final Consumption Expenditure: 2022-23p: Furniture, Floor Coverings and Household Goods data was reported at 11,534.000 AUD mn in Dec 2024. This records an increase from the previous number of 9,496.000 AUD mn for Sep 2024. Australia Household Final Consumption Expenditure: 2022-23p: Furniture, Floor Coverings and Household Goods data is updated quarterly, averaging 5,200.000 AUD mn from Sep 1985 (Median) to Dec 2024, with 158 observations. The data reached an all-time high of 11,775.000 AUD mn in Dec 2021 and a record low of 2,792.000 AUD mn in Mar 1986. Australia Household Final Consumption Expenditure: 2022-23p: Furniture, Floor Coverings and Household Goods data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.A293: SNA08: Household Final Consumption Expenditure: by Industry: Chain Linked: 2022-23p.

Abstract

The Kenya Integrated Household Budget Survey (IHBS) was designed to capture a wide range of socio-economic indicators using an integrated approach as opposed to stand alone surveys. Kenya has a rich history of conducting Household Budget Surveys (HBS) which ordinarily collect data on socio-economic indicators such as demographic, education, health, household consumption, expenditure patterns and sources of household income amongst others. The socio-economic indicators derived from the survey were a milestone in planning and policy information. The Integrated Household Budget Survey also provided statistics for monitoring and evaluating development initiatives and targeted interventions. These indicators complemented the existing baseline information from the 2009 Kenya Population and Housing Census (KPHC) and other surveys.

The survey was conducted over a 12-month period to obtain up-to-date data on a range of socioeconomic indicators used to monitor the implementation of development initiatives. The Survey collected data on household characteristics, housing conditions, education, general health characteristics, nutrition, household income and credit, household transfers, information communication technology, domestic tourism, shocks to household welfare and access to justice. The findings are presented at national, county, rural and urban domains. The specific objectives of the survey include:

1. Computation of poverty/welfare measures (incidence, gap and severity)
2. Updating of national accounts benchmarks e.g. private consumption, informal sector, analysis of household sector
3. Forming a basis for updating household expenditure weights to be used in the development of new consumer Price Index (CPI)
4. Providing quarterly estimates on selected indicators at national level.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey used the fifth national Sample Survey and Evaluation Program (NASSEP V) master frame based on the Kenya Population and Housing Census (KPHC) conducted in 2009. The sample took into consideration the number of households, the area of residence and the domains of analysis.

The sample was stratified and selected in two stages from the master sample frame. Stratification was achieved by separating each county into urban and rural areas; in total 92 sampling strata were created. Samples were selected independently in each sampling stratum by a two stage selection. In this regard, 2400 clusters were sampled with equal probability from 5,360 clusters in NASSEP V. The clusters served as primary sampling units for the selection of ten households per cluster, translating to 24,000 households. A combination of two methods the Paper Assisted Personal Interview (PAPI) and the Computer Assisted Personal Interview (CAPI) were used in the survey.

Sampling deviation

The IHBS was designed to provide estimates for various indicators at the County-level. A total of 50 study domains are envisaged. These are; all the forty-seven counties (Each as a separate domain), urban and rural (each as a separate domain at National level), and lastly the National-level aggregate.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following questionnaires were administered during the survey: 1. Household Questionnaire 2. Community Questionnaire 3. Market Price Questionnaire

Aggregated energy related data. GENERAL INFORMATION Mainfranken Netze GmbH (MFN) and Stadtwerke Würzburg AG (STW) provide aggregated energy consumption and energy production data free of charge by uploading it as regularly as possible to the city's OpenDataPortal, as the greatest possible data transparency is in the general interest to achieve the climate goals. The data come from the network area of the respective grantor of the MFN and the following sectors: Household, commercial, industrial, agricultural, heating electricity, street lighting – municipal data are, as far as possible, issued separately. Municipal data are energy data in which the consumer, operator and/or producer is a municipality. Data on generation are provided according to forms of energy. The following forms of energy - photovoltaic systems on buildings, ground-mounted photovoltaic systems, CHP systems, hydropower - from the following wired media - natural gas, district heating, electricity - are taken into account. This type of data must be published and can also be found in the Market Master Data Register. STW and MFN only transmit aggregated data. Aggregated data are available when at least three consumers/producers are identified per total value. This does not include municipal data. DISCLAIMER OF LIABILITY MFN and STW assume no liability for the accuracy, completeness and availability of the transmitted data. The format of the data may change if the MFN or STW appears to be in a different form. PURPOSE The data should be freely usable by everyone for the purpose of achieving the climate targets. It is not the purpose of this data provision that conclusions can be drawn about individual persons, e.g. by possible aggregation / evaluation / enrichment with other data. DECLARATION OF DATA Sectors have been defined on the basis of contractual partners.Example district heating: Although most district heating in the city centre is used by private individuals for domestic heating and hot water, no district heating is indicated in the table under "Household". The reason for this is that all district heating connections run on commercial homeowners - these are also housing cooperatives, churches, etc., where the private individuals are tenants. description Possible values Settlement year Year of reckoning. As a result, hardly any forecast values will be used. Due to the statutory rolling billing (under the year), the stated energy values are not the consumption in the calendar year. place Concession area in whose network the consumption or the feed-in takes place. (In the case of open-air installations within the boundary, the installation may be physically located in an area but feed into the neighbouring network.) Division Wired media are assigned to a division. Gas, electricity, district heating (including local heating) sector Sectors defined by the Integrated Energy and Climate Concept. The assignment was based on profile types. The profile types are assigned according to the load profile and not according to customer group - therefore these may differ. commerce, household (generally private households), industry, agriculture, street lighting (unless mittles Contracting) Municipal Used to identify municipal customers separately. Local, not local number Number of acceptance points / number of generation plants of the corresponding group. Empty, if less than minimum. Quantity of energy Energy (quantity) in kWh. remark remark OK, not specified (if too few per aggregation) Type of energy Assignment of the type of production to a category. Wind (land), Cogeneration, Photovoltaics (open space), Photovoltaics (buildings), Water conversion Energy quantity of the plant in kWh installed capacity Installed power of the system in kW. Self-consumption Plants have a self-consumption in kWh. Net levy Amount of energy fed into the grid in kWh. (Conversion less self-consumption)

ESG footprint of Synthetic UK Individuals and Businesses

Contains information on various energy usages, housing, and their associated costs on 4000 synthetic UK individuals. It can be used to analyze the trends towards sustainability at the individual level.

This sample data is part of the statistically accurate representation of the UK economy that can be found at https://nayaone.com/digital-twin/. Our mission is democratization and quality data governance in areas where the lack of data is a major hurdle for innovation and progress. To learn more, contact us: contact@nayaone.com

Content

Attributes

Businesses

• borough_county: Districts in the UK (some are counties some are towns)
• primary_sector: SIC - 2007 numerical codes
• entity_trade_name: Trading name of the company
• annual_turnover: Annual turnover split into 4 bands
• entity_status: Active org = 1, Dissolved = 0
• country_of_primary_operation: All UK
• Coal_Consumption_By_Sector(toe): Total coal consumption by company
• Electricity_Consumption_By_Structure(toe): total electricity consumption by the company according th the structure type and location
• Gas_Consumption_By_Structure(toe): total gas consumption by the company according the structure type and location
• Geographic Code: Geocode according th the county of company
• Local Authority: County of the company
• NG_Consumption_By_Sector(toe): Natural gas consumption by compcompany according the structure type and location
• Petrol_Consumption_By_Sector(toe): Petrol consumption by the company according the structure type and location
• Region: UK region where company is lcompany is located
• SIC Group: Industrial classification of company group
• Section: Industrial classification of the company section
• Sector: Sector of the company work
• Structure_Type: Structure type of the company

Individual

• individual_ID: Key
• name: First name + Surname
• sex: Individual's gender
• geography: Geography
• postcode: Postcode
• ethic_group: The ethnic group classification presented is the recommended framework from the 'Harmonised Concepts and Questions for Social Data Sources Primary Standards' for presentation of UK outputs on ethnic group
• nationality: Nationality of the individual
• marital_and_civil_partnership_status: Marital and civil partnership status classifies an individual according to their legal marital or registered same-sex civil
• occupation: The person's occupation relates to their main job and is derived from either their job title or details of the activities involved in their job
• Diesel car: Number of dissel car
• Diesel consumption: Diesel consumption per individual
• Electricity consumption: electricity consumption per individual
• Family type: Type of the family individual living in
• Gas Availability: Gas connection availibility
• Gas consumption: Gas consumption of the individual
• House ID: House ID, per family
• House members: Number of house members in house
• Individual type: Type of the Individual
• Motor cycle: Number of Motor cycle present
• Number of bedrooms: Totol number of bedrooms in the house
• Other fuel car: Number of other fuel cars
• Petrol car: Number of petrol cars
• Petrol consumption: Petrol consumption per indivdual
• Region: UK region where family or house is located

The advancing defossilisation of the energy system requires far-reaching interventions and their sound planning to ensure an efficient, safe and sustainable system transformation. The application of electricity demand models with high temporal and spatial resolution is a key element for evaluating different transformation pathways. As a literature review reveals, however, models and data describing electricity demand are only available in very fragmentary or outdated form. This thesis addresses this research gap and focuses on the development, validation, exemplary application and evaluation of subsector load profiles. The potential benefit is evident: subsector load profiles serve as generic profiles which allow to model national or regional power systems. They are used for demand forecasting, for the planning and design of power generation plants and for the procurement of energy. Moreover, they can be used to analyse efficiency and demand side flexibility potentials of individual subsectors – a field of increasing relevance in the scientific literature. There are multiple fields of application for electricity load profiles spanning across all steps of the value chain. Addressing different research gaps, this thesis is divided into six modules. The first module presents the development of 32 subsector load profiles (TUB BLP) from the sectors of industry as well as commerce, trade and services (CTS). Based on a large number of real metered load profiles, the subsector load profiles are developed using multiple regression and then validated using real data and literature-based load profiles (e.g. VDEW standard load profiles). The performance of the regression model approach is also compared with the model quality of a feed-forward artificial neural network. The accuracy of the subsector load profiles varies between subsectors, which is due to the underlying explainable variance in the data and the subsector-specific heterogeneity. Overall, however, a comparison with real metered load profiles shows in the vast majority of cases a very reasonable model performance according to Lewis' benchmark as well as a mostly significantly higher mapping accuracy of the developed TUB BLP compared to available standard load profiles. In combination with a description of the load characteristics and the demand drivers, the TUB BLP of each subsector were made freely available for further scientific use. In the second module, the TUB BLP are used and evaluated in the Python-based application disaggregator. The disaggregator allows the modelling of electricity demand in Germany in high temporal and spatial resolution. Using demand drivers, the annual electricity consumption from the industrial, commercial and residential sectors is disaggregated to subsectors and counties. Subsequently, the annual electricity consumption is converted into electricity load profiles of quarter-hourly resolution using subsector load profiles. On the one hand, standard load profiles and generic load profiles are used as subsector load profiles; on the other hand, the TUB BLP developed in the first module are used. The model results of the different load profile approaches are compared with real data at federal and county level. It is shown that the disaggregator can reproduce the load behaviour at both federal and county level in a good to very good approximation. In addition, it can be seen that the use of TUB BLP significantly improves the load modelling compared to standard load profiles. There are various possible explanations for remaining structural deviations in the model results: In addition to a possible inaccuracy of the residential profile used, some important subsectors could not be modelled in a distinguished manner in the form of TUB BLP due to a lack of data. In the third module, an engineering-based approach is developed for modelling technology-specific load profiles of five CTS subsectors. Due to the increased sophistication and effort of the engineering-based approach, this approach is applied in modules 3-5 to only five relevant subsectors out of the original 32 considered in module 1. These five subsectors of offices, trade, accommodation, hospitals and education account for about 62 % of the electricity consumption of the CTS sector. Occupancy profiles are developed based on international and national standards (ISO, DIN, SIA), which are converted into load profiles for each application technology in conjunction with technology-specific simultaneity profiles. By means of a literature-based annual electricity demand, a subsector-specific scaling of the load profiles is then carried out. A comparison of the engineering-based subsector load profiles with the TUB BLP developed in the first module allows the adjustment of the weighting of international and national standards as well as individual assumptions to increase the accuracy of the model. As a result, technology-specific load profiles for five subsectors are presented, which represent essential load characteristics and form the foundation for the modelling steps of the next two modules. In the fourth module, the previously developed technology-specific load profiles of five subsectors are projected to the year 2035 with the help of literature-based scenarios. In addition to the efficiency development of the individual application technologies, a technology shift from night storage heaters to heat pumps with a corresponding profile change is also taken into account. It can be seen that the resulting (cumulative) load profiles alter in some subsectors. The projected load profiles of some subsectors show more pronounced load peaks. The energy consumption shares of individual technologies also change, which in turn influences the load flexibility potentials in the fifth module. In the fifth module, technical demand side flexibility (DSF) potentials of the above five CTS subsectors are quantified in high temporal and spatial resolution. The DSF potentials are specified per subsector and application technology (air conditioning, ventilation, process cooling, space heating and hot water) for the years 2018 and 2035 and described in terms of minimum/maximum switchable loads, minimum/maximum shiftable energy quantities, shift duration and temporal availabilities. The five subsectors are responsible for about 74 % of the technical DSF potential of the entire CTS sector. A comparison with literature values underlines the plausibility of the chosen approach. The high switchable loads identified for the subsectors offices and trade, as well as the temporally stable shiftable energy quantities of hospitals and accommodation, can make a cost-effective contribution to the reduction of the residual load, the avoidance of grid bottlenecks and the integration of renewable energies in the overall system. In the last module, the beneficial applicability of developed subsector load profiles is demonstrated in two use cases: In the first use case, the substitution of old standard load profiles by newly developed TUB BLP is assessed for the electricity procurement and balancing group management. Therefore, the model outputs of the disaggregator (once using standard load profiles only, once using TUB BLP) are priced on the spot market, simulating a specific procurement strategy. Any model deviations that arise between the disaggregator output and real reference loads are considered by the imbalance settlement price. The assessment comfirms that the total costs from procurement and balancing energy are significantly reduced for the entire system by using TUB BLP (and replacing standard load profiles) in the outlined case. However, the assessment also shows that arbitrage profits, which result from short-term trading or imbalance settlement, are smaller in the majority of cases through the application of TUB BLP. These unilaterally generated arbitrage profits result in an incentive, especially for distribution system operators in the synthetic load profile procedure, to continue to use partially outdated standard load profiles and not to switch to new, more accurate subsector load profiles. In the second use case, the flexibility potentials identified in the engineering-based modelling approach are economically evaluated in their use for peak load reduction. For this purpose, Germany's residual load in 2018 is compared with the temporally high-resolution DSF potentials in order to determine the maximum peak load reduction through load shifting. Technical restrictions of the load reduction potentials and shiftable energy quantities are taken into account. The maximum peak load reduction is evaluated with the annual power costs for gas turbine power plants that can be replaced by using the CTS DSF potentials. These cost savings are compared with the estimated costs for exploiting the DSF potentials. It is shown that commercial DSF offers a considerable cost saving potential reducing necessary peak load capacity. In addition, other use cases promise further economic benefits.