1. ABOUT THE DATASET ------------ Title: MERRA2 derived time series of GB telecommunications infrastructure electricity load, using historical daily surface temperature 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: MERRA2 re-analysis data (1981-2023, 2m temperature) is used to calculate the daily electricity load of telecommunications infrastructure in GB, London, and North Scotland. The model has been trained on BT group plc data, 2016-2020, to investigate the extent of weather-variability in causing extreme demand events in heating- and cooling-driven infrastructure. Cite as: 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 Related publication: 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 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 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.ipynb (python notebook, explains and demonstrates the model, can be used to generate new outputs) infrastructure_electricity_load.png (example schematic figure of the input/output data) models (model coefficients, trained on BT group plc. data 2016-2020) modules (python helper functions, used by demand-model.ipynb) outputs (model outputs in CSV format) temperature (model inputs in CSV format) 5. METHODS ----------- A detailed write-up of the methods used can be found in the included demand-model python notebook (an interactive way to familiarise with the data and methods, and generate new outputs). A PDF copy has also been included. Further explanation of the model development and application can be found in the accompanying publication: Fallon et al. (2023), https://doi.org/10.1002/met.2158 6. DATASETS ----------- We have made use of the following datasets: 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) The model demand coefficients, and weekday seasonality patterns are derived from a proprietary dataset of infrastructure electricity demand, BT Group plc.