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...

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Published in:Environmental Research Letters
Main Authors: Samuel Li, Ryan Sriver, Douglas E Miller
Format: Article in Journal/Newspaper
Language:English
Published: IOP Publishing 2023
Subjects:
Q
Online Access:https://doi.org/10.1088/1748-9326/acd072
https://doaj.org/article/a5bb7fc96a73454dbaf1d0d5d7b4e4d1
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spelling 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
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