DECC NEED ============ Extract & analyse data from the anonymised & released versions of DECC's NEED dataset. Original 2014 'End User License' version of the data: * available from: UK DATA ARCHIVE: Study Number 7518 - National Energy Efficiency Data-Framework, 2014 http://discover.ukdataservice.ac.uk/catalogue/?sn=7518 * Detailed documentation: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/332169/need_anonymised_dataset_accompanying_documentation.pdf * Full coding details of variables at: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/315189/need_dataset_look_ups.xlsx You may find that the scripts also work with the Public Use File (https://www.gov.uk/government/statistics/national-energy-efficiency-data-framework-need-anonymised-data-2014) but I have not tested this. ###Terms of Use GPL: V2 - http://choosealicense.com/licenses/gpl-2.0/ See license file for details. [YMMV](http://en.wiktionary.org/wiki/YMMV) Notes (mostly to self) ---------------------- * gas kwh are weather corrected within the 10 DNO distribution zones before delivery to DECC * The End User License file (EULF) dataset is a sample of just over 4 million households * EULF is a semi-random sample of the 8m records which have an Energy Performance Certificate. * It includes only those with valid values on key variables (Property Age, Property Type, Floor Area Band and Energy Efficiency Band) and (especially) valid observations for electricity in 2012. * Records were selected based on the frequency of household type in the dataset relative to the total dwelling stock so that uncommon property types (e.g. older detached properties) are over-represented and common types (e.g. flats where turnover is high) are under-represented. The supplied weight corrects for this for descriptive analysis. * Implications for sample bias unclear - there may be other systematic biases not captured by the weight? * UPRN = unique property reference = linkage mechanism across EPCs, gas/electricity data and EST data on energy efficiency installations (uses AddressBase) * PV installs added for 2015 report - see https://www.gov.uk/government/statistics/national-energy-efficiency-data-framework-need-report-summary-of-analysis-2015 * Bias caused by linkage failure is unknown although the DECC NEED Data Framework report from 2013 suggest match rates of 94%-100% (https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/209264/Annex_B_-_Quality_Assurance.pdf) * Both gas and electricity consumption are rounded and the rounding range ('to nearest n') increases through the distributions (see https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/315189/need_dataset_look_ups.xlsx). The reasons for this are explained in the consultation response at https://www.gov.uk/government/consultations/national-energy-efficiency-data-framework-making-data-available * the Gcons*valid variable codes: * G = Gas consumption invalid, greater than 50,000 * L = Gas consumption invalid, less than 100 * M = Gas consumption data is missing in source data * 0 = Property does not have a gas connection * V = Valid gas consumption (between 100 and 50,000 inclusive) * NB - there are valid gas readings of '0' which presumably were > 100 but < 249 (first gas 'heap' = 'nearest 500') * the Econs*valid variable codes: * G Electricity consumption invalid, greater than 25,000 (DECC lookup table says 50,000) * L Electricity consumption invalid, less than 100 * M Electricity consumption data is missing in source dataset * V Valid electricity consumption (between 100 and 25,000 inclusive) Notes to DECC (!) ----------------- * ideally could set missing to -99 to aid re-coding and avoid unpleasant surprises in naive analysis? * can the consumption rounding be constant through the distributions? * check coding of Gcons ref 0 values for 'valid' cases? * distinguish between electric & 'other' heating in 'main heating fuel'?