Skillful prediction of UK seasonal energy consumption based on surface climate information
Abstract Climate conditions affect winter heating demand in areas that experience harsh winters. Skillful energy demand prediction provides useful information that may be a helpful component in ensuring a reliable energy supply, protecting vulnerable populations from cold weather, and reducing exces...
Published in: | Environmental Research Letters |
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crioppubl:10.1088/1748-9326/acd072 2024-06-02T08:02:33+00:00 Skillful prediction of UK seasonal energy consumption based on surface climate information Li, Samuel Sriver, Ryan Miller, Douglas E University of Illinois Campus Honors Program 2023 http://dx.doi.org/10.1088/1748-9326/acd072 https://iopscience.iop.org/article/10.1088/1748-9326/acd072 https://iopscience.iop.org/article/10.1088/1748-9326/acd072/pdf unknown IOP Publishing http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Letters volume 18, issue 6, page 064007 ISSN 1748-9326 journal-article 2023 crioppubl https://doi.org/10.1088/1748-9326/acd072 2024-05-07T13:55:27Z Abstract Climate conditions affect winter heating demand in areas that experience harsh winters. Skillful energy demand prediction provides useful information that may be a helpful component in ensuring a reliable energy supply, protecting vulnerable populations from cold weather, and reducing excess energy waste. Here, we develop a statistical model that predicts winter seasonal energy consumption over the United Kingdom using a multiple linear regression technique based on multiple sources of climate information from the previous fall season. We take the autumn conditions of Arctic sea-ice concentration, stratospheric circulation, and sea-surface temperature as predictors, which all influence North Atlantic oscillation (NAO) variability as reported in a previous study. The model predicts winter seasonal gas and electricity consumption two months in advance with a statistically significant correlation between the predicted and observed time series. To extend the analysis beyond the relatively short time scale of gas and electricity data availability, we also analyze predictability of an energy demand proxy, heating degree days (HDDs), for which the model also demonstrates skill. The predictability of energy consumption can be attributed to the predictability of the NAO and the significant correlation of energy consumption with surface air temperature, dew point depression, and wind speed. We further found skillful prediction of these surface climate variables and HDDs over many areas where the NAO is influential, implying the predictability of energy demand in these regions. The simple statistical model demonstrates the usefulness of fall climate observations for predicting winter season energy demand prediction with a wide range of potential applications across energy-related sectors. Article in Journal/Newspaper Arctic North Atlantic North Atlantic oscillation Sea ice IOP Publishing Arctic Environmental Research Letters 18 6 064007 |
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Abstract Climate conditions affect winter heating demand in areas that experience harsh winters. Skillful energy demand prediction provides useful information that may be a helpful component in ensuring a reliable energy supply, protecting vulnerable populations from cold weather, and reducing excess energy waste. Here, we develop a statistical model that predicts winter seasonal energy consumption over the United Kingdom using a multiple linear regression technique based on multiple sources of climate information from the previous fall season. We take the autumn conditions of Arctic sea-ice concentration, stratospheric circulation, and sea-surface temperature as predictors, which all influence North Atlantic oscillation (NAO) variability as reported in a previous study. The model predicts winter seasonal gas and electricity consumption two months in advance with a statistically significant correlation between the predicted and observed time series. To extend the analysis beyond the relatively short time scale of gas and electricity data availability, we also analyze predictability of an energy demand proxy, heating degree days (HDDs), for which the model also demonstrates skill. The predictability of energy consumption can be attributed to the predictability of the NAO and the significant correlation of energy consumption with surface air temperature, dew point depression, and wind speed. We further found skillful prediction of these surface climate variables and HDDs over many areas where the NAO is influential, implying the predictability of energy demand in these regions. The simple statistical model demonstrates the usefulness of fall climate observations for predicting winter season energy demand prediction with a wide range of potential applications across energy-related sectors. |
author2 |
University of Illinois Campus Honors Program |
format |
Article in Journal/Newspaper |
author |
Li, Samuel Sriver, Ryan Miller, Douglas E |
spellingShingle |
Li, Samuel Sriver, Ryan Miller, Douglas E Skillful prediction of UK seasonal energy consumption based on surface climate information |
author_facet |
Li, Samuel Sriver, Ryan Miller, Douglas E |
author_sort |
Li, Samuel |
title |
Skillful prediction of UK seasonal energy consumption based on surface climate information |
title_short |
Skillful prediction of UK seasonal energy consumption based on surface climate information |
title_full |
Skillful prediction of UK seasonal energy consumption based on surface climate information |
title_fullStr |
Skillful prediction of UK seasonal energy consumption based on surface climate information |
title_full_unstemmed |
Skillful prediction of UK seasonal energy consumption based on surface climate information |
title_sort |
skillful prediction of uk seasonal energy consumption based on surface climate information |
publisher |
IOP Publishing |
publishDate |
2023 |
url |
http://dx.doi.org/10.1088/1748-9326/acd072 https://iopscience.iop.org/article/10.1088/1748-9326/acd072 https://iopscience.iop.org/article/10.1088/1748-9326/acd072/pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic North Atlantic North Atlantic oscillation Sea ice |
genre_facet |
Arctic North Atlantic North Atlantic oscillation Sea ice |
op_source |
Environmental Research Letters volume 18, issue 6, page 064007 ISSN 1748-9326 |
op_rights |
http://creativecommons.org/licenses/by/4.0 https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1748-9326/acd072 |
container_title |
Environmental Research Letters |
container_volume |
18 |
container_issue |
6 |
container_start_page |
064007 |
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1800747039711035392 |