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Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data

Yeran Sun, Shaohua Wang, Xucai Zhang, Ting On Chan, Wenjie Wu

Energy, Volume: 226, Start page: 120351

Swansea University Author: Yeran Sun

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DOI (Published version): 10.1016/j.energy.2021.120351

Abstract

To implement a new mixed approach for electricity energy consumption estimates, this study aimed to estimate country-wide local-scale electricity consumption by combining demographic, remote sensing, and social sensing data. Specifically, England-wide local-scale electricity energy consumption, incl...

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Published in: Energy
ISSN: 0360-5442
Published: Elsevier BV 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57075
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first_indexed 2021-06-09T15:58:08Z
last_indexed 2021-06-11T03:22:52Z
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spelling 2021-06-10T16:02:05.3422675 v2 57075 2021-06-09 Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data 10382520ce790248e1be61a6a9003717 Yeran Sun Yeran Sun true false 2021-06-09 To implement a new mixed approach for electricity energy consumption estimates, this study aimed to estimate country-wide local-scale electricity consumption by combining demographic, remote sensing, and social sensing data. Specifically, England-wide local-scale electricity energy consumption, including domestic and non-domestic ones, was estimated based on population in combination with nighttime light intensity or/and tweet volume. Moreover, to improve the explanatory power of statistical regression models, this study applied a newly developed spatial regression model (i.e., the ‘random effects eigenvector spatial filtering’ model) to the estimation of electricity energy consumption in comparison with conventional spatial regression models used in relevant studies. The spatial regression model used was further compared with machine learning and deep learning models (i.e., random forest and long short-term memory models). The empirical results uncover that: 1) the electricity energy consumption can be best explained by population in combination with both the nighttime light intensity and tweet volume; 2) the domestic electricity energy consumption can be better explained than its non-domestic counterpart; 3) the ‘random effects eigenvector spatial filtering’ models appear to outperform the conventional spatial regression models; and 4) the performance of the ‘random effects eigenvector spatial filtering’ models is similar to that of the random forest models and is lower than that of the long short-term memory models. Journal Article Energy 226 120351 Elsevier BV 0360-5442 Electricity energy consumption; Twitter data; Nighttime light imagery; SNPP-VIIRS; Random effects eigenvector spatial filtering 1 7 2021 2021-07-01 10.1016/j.energy.2021.120351 COLLEGE NANME COLLEGE CODE Swansea University 2021-06-10T16:02:05.3422675 2021-06-09T16:47:49.2453638 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography Yeran Sun 1 Shaohua Wang 2 Xucai Zhang 3 Ting On Chan 4 Wenjie Wu 5 57075__20117__063da7c8636b4cbb8b6a7f4d2c07c50e.pdf manuscript_R2 - no changes marked.pdf 2021-06-10T11:07:43.3106130 Output 1045106 application/pdf Accepted Manuscript true 2022-03-12T00:00:00.0000000 ©2021 All rights reserved. All article content, except where otherwise noted, is licensed under a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND) true eng https://creativecommons.org/licenses/by-nc-nd/4.0/
title Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data
spellingShingle Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data
Yeran Sun
title_short Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data
title_full Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data
title_fullStr Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data
title_full_unstemmed Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data
title_sort Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data
author_id_str_mv 10382520ce790248e1be61a6a9003717
author_id_fullname_str_mv 10382520ce790248e1be61a6a9003717_***_Yeran Sun
author Yeran Sun
author2 Yeran Sun
Shaohua Wang
Xucai Zhang
Ting On Chan
Wenjie Wu
format Journal article
container_title Energy
container_volume 226
container_start_page 120351
publishDate 2021
institution Swansea University
issn 0360-5442
doi_str_mv 10.1016/j.energy.2021.120351
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
description To implement a new mixed approach for electricity energy consumption estimates, this study aimed to estimate country-wide local-scale electricity consumption by combining demographic, remote sensing, and social sensing data. Specifically, England-wide local-scale electricity energy consumption, including domestic and non-domestic ones, was estimated based on population in combination with nighttime light intensity or/and tweet volume. Moreover, to improve the explanatory power of statistical regression models, this study applied a newly developed spatial regression model (i.e., the ‘random effects eigenvector spatial filtering’ model) to the estimation of electricity energy consumption in comparison with conventional spatial regression models used in relevant studies. The spatial regression model used was further compared with machine learning and deep learning models (i.e., random forest and long short-term memory models). The empirical results uncover that: 1) the electricity energy consumption can be best explained by population in combination with both the nighttime light intensity and tweet volume; 2) the domestic electricity energy consumption can be better explained than its non-domestic counterpart; 3) the ‘random effects eigenvector spatial filtering’ models appear to outperform the conventional spatial regression models; and 4) the performance of the ‘random effects eigenvector spatial filtering’ models is similar to that of the random forest models and is lower than that of the long short-term memory models.
published_date 2021-07-01T04:12:32Z
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score 11.036706

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