{"id":150,"date":"2020-05-22T15:49:17","date_gmt":"2020-05-22T14:49:17","guid":{"rendered":"https:\/\/research.reading.ac.uk\/met-energy\/?page_id=150"},"modified":"2024-07-19T17:34:39","modified_gmt":"2024-07-19T16:34:39","slug":"data","status":"publish","type":"page","link":"https:\/\/research.reading.ac.uk\/met-energy\/data\/","title":{"rendered":"Models and data"},"content":{"rendered":"<p>&nbsp;<\/p>\n<h3>Reconstructions, forecasts and climate projections of electricity demand and renewable generation<\/h3>\n<p>Over the last several years, we have developed many different &#8220;meteorologically-based&#8221; datasets of nationally-aggregated demand, wind power and solar PV generation over Europe at sub-daily frequency. \u00a0These now span the recent historic period (back to ~1950), future climate projections (up to ~2050) and archived sub-seasonal forecasts (multi-week ahead ensemble re-forecasts over the period 1996-2015).<\/p>\n<p>Many of these datasets are freely available for use (click on the DOI links below for information\/access):<\/p>\n<ul>\n<li>Bloomfield, Hannah and Brayshaw, David (2021): Future climate projections of surface weather variables, wind power, and solar power capacity factors across North-West Europe. University of Reading. Dataset. <a href=\"https:\/\/researchdata.reading.ac.uk\/id\/eprint\/331\">https:\/\/researchdata.reading.ac.uk\/id\/eprint\/331<\/a><\/li>\n<p>&nbsp;<\/p>\n<li>Bloomfield, Hannah and Brayshaw, David (2021): ERA5 derived time series of European aggregated surface weather variables, wind power, and solar power capacity factors: hourly data from 1950-2020.<br \/>\nUniversity of Reading. Dataset. <a href=\"https:\/\/researchdata.reading.ac.uk\/id\/eprint\/321\">https:\/\/researchdata.reading.ac.uk\/id\/eprint\/321<\/a><\/li>\n<p>&nbsp;<\/p>\n<li>Gonzalez, Paula, Bloomfield, Hannah, Brayshaw, David and Charlton-Perez, Andrew (2020): Sub-seasonal forecasts of European electricity demand, wind power and solar power generation. University of Reading. Dataset. <a href=\"http:\/\/dx.doi.org\/10.17864\/1947.275\">http:\/\/dx.doi.org\/10.17864\/1947.275<\/a><\/li>\n<p>&nbsp;<\/p>\n<li>Bloomfield, Hannah, Brayshaw, David and Charlton-Perez, Andrew (2020): ERA5 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation: hourly data from 1979-2019. University of Reading. Dataset. <a href=\"http:\/\/dx.doi.org\/10.17864\/1947.275\">http:\/\/dx.doi.org\/10.17864\/1947.275<\/a><\/li>\n<p>&nbsp;<\/p>\n<li><span class=\"person_name\">Bloomfield,<\/span> <span class=\"person_name\">Hannah<\/span>, <span class=\"person_name\">Brayshaw, David<\/span> and <span class=\"person_name\">Charlton-Perez, Andrew<\/span> (2020): MERRA2 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation. University of Reading. Dataset.<a href=\"http:\/\/dx.doi.org\/10.17864\/1947.239\" target=\"_blank\" rel=\"noopener noreferrer\">http:\/\/dx.doi.org\/10.17864\/1947.239<\/a><\/li>\n<p>&nbsp;<\/p>\n<li>Bloomfield, Hannah, Brayshaw, David and Charlton-Perez, Andrew (2019): ERA5 derived time series of European country-aggregate electricity demand, wind power generation and solar power generation. University of Reading. Dataset. <a href=\"http:\/\/dx.doi.org\/10.17864\/1947.227\">http:\/\/dx.doi.org\/10.17864\/1947.227<\/a><\/li>\n<p>&nbsp;<\/p>\n<li>Drew, Daniel, Bloomfield, Hannah, Coker, Phil, Barlow, Janet and Brayshaw, David (2019): MERRA derived hourly time series of GB-aggregated wind power, solar power and demand. University of Reading. Dataset. <a href=\"http:\/\/dx.doi.org\/10.17864\/1947.191\">http:\/\/dx.doi.org\/10.17864\/1947.191<\/a><\/li>\n<p>&nbsp;<\/p>\n<li>Drew, Daniel, Brayshaw, David, Barlow, Janet and Coker, Phil (2016): An hourly time series of GB-aggregated wind power generation from 1980-2013, based on a future distribution of wind farms with a high level of offshore capacity. University of Reading. Dataset. <a href=\"http:\/\/dx.doi.org\/10.17864\/1947.75\">http:\/\/dx.doi.org\/10.17864\/1947.75<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<p>An earlier dataset reconstructing GB-aggregated wind power generation is available <a href=\"http:\/\/centaur.reading.ac.uk\/37430\/\">here<\/a>\u00a0and a brief introductory description is provided here. \u00a0This dataset corresponds to that used in <a href=\"https:\/\/doi.org\/10.1016\/j.renene.2014.10.024\">Cannon et al (2015),\u00a0<em>Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain.<\/em> Renewable Energy, 75. pp. 767-778<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h3>GB telecommunications infrastructure electricity load<\/h3>\n<p><a href=\"https:\/\/doi.org\/10.1002\/met.2158\" class=\"broken_link\">Fallon et al (2023)<\/a>\u00a0present a new framework for using weather\u2010sensitive surplus power reserves in critical infrastructure based on synthesised load data for BT in the UK derived from the MERRA2 reanalysis. The data used in that paper are available <a href=\"https:\/\/doi.org\/10.17864\/1947.000533\">here<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h3>Efficient sampling of climate uncertainty for power systems modelling: tools, code and examples<\/h3>\n<p><a href=\"https:\/\/doi.org\/10.1016\/j.apenergy.2019.04.110\">Hilbers et al (2019)<\/a>\u00a0present a novel &#8220;importance subsampling&#8221; method to incorporate large-volumes of climate data into complex and computationally expensive power system models (e.g., Unit Commitment \/ Economic Dispatch, Generation and Transmission Expansion Planning). \u00a0The data and tools used in that paper are available <a href=\"https:\/\/github.com\/ahilbers\/importance_subsampling\">here<\/a>.<\/p>\n<p>Extensions to the methodology can be found in\u00a0<a href=\"https:\/\/doi.org\/10.1109\/PMAPS47429.2020.9183591\">Hilbers et al (2020)<\/a>, and a set of simple &#8220;test case&#8221; and &#8220;tutorial&#8221; model can be found <a href=\"https:\/\/github.com\/ahilbers\/renewable_test_PSMs\">here<\/a>.<\/p>\n<p>Additional tools for exploring sampling uncertainty using computationally efficient &#8220;fast&#8221; bootstrap methods were developed in\u00a0<a href=\"https:\/\/doi.org\/10.1109\/TPWRS.2020.3031187\">Hilbers et al (2021)<\/a>, with code\/data\/model available <a href=\"https:\/\/github.com\/ahilbers\/bootstrap_uncertainty_quantification\">here<\/a>.<\/p>\n<p>&nbsp;<\/p>\n<p>&nbsp;<\/p>\n<h3>Quasi-stationary wave climatology<\/h3>\n<p>Large-scale quasi-stationary atmospheric waves (QSWs) have long been known to be associated with weather extremes in the midlatitude, such as heatwaves (e.g., Europe, 2003). \u00a0The NERC ODYSEA project produced a QSW climatology, as described in <a href=\"http:\/\/centaur.reading.ac.uk\/76564\/\">Wolf et al 2018<\/a>\u00a0and\u00a0\u00a0<a href=\"https:\/\/doi.org\/10.1175\/JCLI-D-18-0751.1\">Wolf et al 2020<\/a>. \u00a0The climatology dataset is available for download via the CEDA archive <a href=\"http:\/\/dx.doi.org\/10.5285\/c0c7998800414e46b6823dc75751bb4c\">here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>&nbsp; Reconstructions, forecasts and climate projections of electricity demand and renewable generation Over the last several years, we have developed many different &#8220;meteorologically-based&#8221; datasets of nationally-aggregated demand, wind power and&#8230;<a class=\"read-more\" href=\"&#104;&#116;&#116;&#112;&#115;&#58;&#47;&#47;&#114;&#101;&#115;&#101;&#97;&#114;&#99;&#104;&#46;&#114;&#101;&#97;&#100;&#105;&#110;&#103;&#46;&#97;&#99;&#46;&#117;&#107;&#47;&#109;&#101;&#116;&#45;&#101;&#110;&#101;&#114;&#103;&#121;&#47;&#100;&#97;&#116;&#97;&#47;\">Read More ><\/a><\/p>\n","protected":false},"author":73,"featured_media":0,"parent":0,"menu_order":9,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"__cvm_playback_settings":[],"__cvm_video_id":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-150","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.8.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Models and data - Energy Meteorology Research Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/research.reading.ac.uk\/met-energy\/data\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Models and data - Energy Meteorology Research Group\" \/>\n<meta property=\"og:description\" content=\"&nbsp; 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