Skillful prediction of UK seasonal energy consumption based on surface climate information
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...
Published in: | Environmental Research Letters |
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2023
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Online Access: | https://doi.org/10.1088/1748-9326/acd072 https://doaj.org/article/a5bb7fc96a73454dbaf1d0d5d7b4e4d1 |
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ftdoajarticles:oai:doaj.org/article:a5bb7fc96a73454dbaf1d0d5d7b4e4d1 2023-09-05T13:17:41+02:00 Skillful prediction of UK seasonal energy consumption based on surface climate information Samuel Li Ryan Sriver Douglas E Miller 2023-01-01T00:00:00Z https://doi.org/10.1088/1748-9326/acd072 https://doaj.org/article/a5bb7fc96a73454dbaf1d0d5d7b4e4d1 EN eng IOP Publishing https://doi.org/10.1088/1748-9326/acd072 https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/acd072 1748-9326 https://doaj.org/article/a5bb7fc96a73454dbaf1d0d5d7b4e4d1 Environmental Research Letters, Vol 18, Iss 6, p 064007 (2023) energy demand prediction climate prediction statistical modeling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 article 2023 ftdoajarticles https://doi.org/10.1088/1748-9326/acd072 2023-08-13T00:36:54Z 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 Directory of Open Access Journals: DOAJ Articles Arctic Environmental Research Letters 18 6 064007 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
energy demand prediction climate prediction statistical modeling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
spellingShingle |
energy demand prediction climate prediction statistical modeling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 Samuel Li Ryan Sriver Douglas E Miller Skillful prediction of UK seasonal energy consumption based on surface climate information |
topic_facet |
energy demand prediction climate prediction statistical modeling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
Samuel Li Ryan Sriver Douglas E Miller |
author_facet |
Samuel Li Ryan Sriver Douglas E Miller |
author_sort |
Samuel Li |
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 |
https://doi.org/10.1088/1748-9326/acd072 https://doaj.org/article/a5bb7fc96a73454dbaf1d0d5d7b4e4d1 |
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, Vol 18, Iss 6, p 064007 (2023) |
op_relation |
https://doi.org/10.1088/1748-9326/acd072 https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/acd072 1748-9326 https://doaj.org/article/a5bb7fc96a73454dbaf1d0d5d7b4e4d1 |
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 |
_version_ |
1776198766733295616 |