1. ABOUT THE DATASET ------------ Title: Model of GB telecommunications electricity load using UKCP18 climate projections Creators: - James Fallon (1) https://orcid.org/0000-0002-6321-7456 - David Brayshaw (1) https://orcid.org/0000-0002-3927-4362 - John Methven (1) https://orcid.org/0000-0002-7636-6872 - Kjeld Jensen (2) https://orcid.org/0000-0001-9487-120X - Louise Krug (2) Organisations: 1) University of Reading, 2) BT Group plc. Rights-holder: University of Reading, BT Group plc. Publication Year: 2024 Description: GB telecommunications infrastructure electricity load is modelled in simulated climate scenarios (1.5C, 2.0C, etc.) using a bias-adjustment of climate model outputs (UKCP18 perturbed physics ensemble, regional downscaling). Several bias-adjustments are made available, including 'mean' bias adjustment and 'Quantile Delta Mapping'. A justification of different approaches is made in an accompanying paper (in progress). The model coefficients are taken from the dataset "MERRA2 derived time series of GB telecommunications infrastructure electricity load, using historical daily surface temperature" https://doi.org/10.17864/1947.000533 Cite as: Fallon, J., Brayshaw, D., Methven, J., Jensen, K., & Krug, L. (2024): Model of GB telecommunications electricity load using meteorological UKCP18 climate projections. University of Reading. Dataset. https://doi.org/10.17864/1947.001339 Related publication: Fallon, J. C., Brayshaw, D. J., Methven, J., Jensen, K., Krug, L. (in progress): Reserve Power Design in Future Climates: Bias Adjustment Approaches for Regional Climate Projections. 2. TERMS OF USE ------------ Copyright 2024 University of Reading, BT Group plc. This dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0 3. PROJECT AND FUNDING INFORMATION ------------ Title: NERC SCENARIO DTP (PhD project), with CASE funding from BT Group plc. Dates: 2019-09-30 to 2024-04-30 Funding organisation: Natural Environment Research Council Grant no.: 2285060 4. CONTENTS ------------ Pre-computed outputs of GB telecommunications infrastructure electricity load in different climate scenarios are stored in the `outputs` directory. Additionally, this dataset contains model coefficients, code, and the relevant temperature timeseries data for re-constructing the temperature-driven models of infrastructure electricity load. demand-model-ukcp18.ipynb (python notebook, explains and demonstrates the model, can be used to generate new outputs) models (model coefficients, trained on BT group plc. data 2016-2020) modules (python helper functions, used by demand-model-ukcp18.ipynb) outputs (model outputs in netCDF4 format) temperature (model inputs in netCDF4, CSV format) 5. METHODS ----------- Application of the model, simulating climate variability and climate change, and discussion of bias-adjustment can be found in the accompanying paper: + Fallon, J. C., Brayshaw, D. J., Methven, J., Jensen, K., Krug, L. (in preparation): Reserve Power Design in Future Climates: Bias Adjustment Approaches for Regional Climate Projections. For further information on using the UK Climate Projections, please see the documents: + UKCP18 Guidance: Caveats and limitations https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp18-guidance---caveats-and-limitations.pdf + Lowe, Jason A., et al. (2018) UKCP18 science overview report. Met Office Hadley Centre, UK. https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP18-Overview-report.pdf) A detailed write-up of the original infrastructure electricity load model development can be found in the published paper and accompanying dataset: + Fallon, J. C., Brayshaw, D. J., Methven, J., Jensen, K., Krug, L. (2023): A new framework for using weather-sensitive surplus power reserves in critical infrastructure. Meteorological Applications. 30(6), e2158. https://doi.org/10.1002/met.2158 + Fallon, J., Brayshaw, D., Methven, J., Jensen, K., & Krug, L. (2024): MERRA2 derived time series of GB telecommunications infrastructure electricity load, using historical daily surface temperature. University of Reading. Dataset. https://doi.org/10.17864/1947.000533 6. DATASETS ----------- We have made use of the following datasets: | MERRA2 Global Modeling and Assimilation Office (GMAO) (2015), MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics V5.12.4, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 2024-01-24, [doi:10.5067/VJAFPLI1CSIV](https://doi.org/10.5067/VJAFPLI1CSIV) | UKCP18 Variables from regional projections (12km) over UK for daily data https://ukclimateprojections-ui.metoffice.gov.uk/products/LS3_Subset_02 Variables from global projections (60km) over UK for monthly, seasonal or annual data https://ukclimateprojections-ui.metoffice.gov.uk/products/LS2_Subset_01 | HadCRUT5 Morice, C.P., J.J. Kennedy, N.A. Rayner, J.P. Winn, E. Hogan, R.E. Killick, R.J.H. Dunn, T.J. Osborn, P.D. Jones and I.R. Simpson (2021) An updated assessment of near-surface temperature change from 1850: the HadCRUT5 dataset. Journal of Geophysical Research (Atmospheres) https://doi.org/10.1029/2019JD032361 HadCRUT.5.0.0.0 data were obtained from http://www.metoffice.gov.uk/hadobs/hadcrut5 on 2023-02-27 and are © British Crown Copyright, Met Office 2023, provided under an Open Government License, http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 | Electricity Load Coefficients The model demand coefficients, and weekday seasonality patterns are derived from a proprietary dataset of infrastructure electricity demand, BT Group plc. and previously published in University of Reading Dataset "MERRA2 derived time series of GB telecommunications infrastructure electricity load, using historical daily surface temperature" (https://doi.org/10.17864/1947.000533).