IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v226y2021ics0360544221006009.html
   My bibliography  Save this article

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

Author

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

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, 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.

Suggested Citation

  • Sun, Yeran & Wang, Shaohua & Zhang, Xucai & Chan, Ting On & Wu, Wenjie, 2021. "Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data," Energy, Elsevier, vol. 226(C).
  • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006009
    DOI: 10.1016/j.energy.2021.120351
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221006009
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.120351?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Laubscher, Ryno, 2019. "Time-series forecasting of coal-fired power plant reheater metal temperatures using encoder-decoder recurrent neural networks," Energy, Elsevier, vol. 189(C).
    2. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Yang, Chengshu & Li, Linyi & Huang, Chang & Chen, Zuoqi & Liu, Rui & Wu, Jianping, 2016. "Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 184(C), pages 450-463.
    3. Tomoki Nakaya, 2000. "An Information Statistical Approach to the Modifiable Areal Unit Problem in Incidence Rate Maps," Environment and Planning A, , vol. 32(1), pages 91-109, January.
    4. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    5. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    6. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    7. Jinpei Ou & Xiaoping Liu & Xia Li & Meifang Li & Wenkai Li, 2015. "Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.
    8. Yongxia Ding & Wei Qu & Shuwen Niu & Man Liang & Wenli Qiang & Zhenguo Hong, 2016. "Factors Influencing the Spatial Difference in Household Energy Consumption in China," Sustainability, MDPI, vol. 8(12), pages 1-20, December.
    9. Blázquez Gomez, Leticia M. & Filippini, Massimo & Heimsch, Fabian, 2013. "Regional impact of changes in disposable income on Spanish electricity demand: A spatial econometric analysis," Energy Economics, Elsevier, vol. 40(S1), pages 58-66.
    10. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    11. Yu, Huayi, 2012. "The influential factors of China's regional energy intensity and its spatial linkages: 1988–2007," Energy Policy, Elsevier, vol. 45(C), pages 583-593.
    12. Xie, Yanhua & Weng, Qihao, 2016. "Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries," Energy, Elsevier, vol. 100(C), pages 177-189.
    13. Cabral, Joilson de Assis & Legey, Luiz Fernando Loureiro & Freitas Cabral, Maria Viviana de, 2017. "Electricity consumption forecasting in Brazil: A spatial econometrics approach," Energy, Elsevier, vol. 126(C), pages 124-131.
    14. Hao, Yu & Liu, Yiming & Weng, Jia-Hsi & Gao, Yixuan, 2016. "Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial econometric analysis," Energy, Elsevier, vol. 114(C), pages 1214-1223.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    2. Zhe Li & Feng Wu & Huiqiang Ma & Zhanjun Xu & Shaohua Wang, 2022. "Spatiotemporal Evolution and Relationship between Night Time Light and Land Surface Temperature: A Case Study of Beijing, China," Land, MDPI, vol. 11(4), pages 1-24, April.
    3. Wang, Delu & Gan, Jun & Mao, Jinqi & Chen, Fan & Yu, Lan, 2023. "Forecasting power demand in China with a CNN-LSTM model including multimodal information," Energy, Elsevier, vol. 263(PE).
    4. Du, Mengbing & Ruan, Jianhui & Zhang, Li & Niu, Muchuan & Zhang, Zhe & Xia, Lang & Qian, Shuangyue & Chen, Chuchu, 2024. "China's local-level monthly residential electricity power consumption monitoring," Applied Energy, Elsevier, vol. 359(C).
    5. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).
    6. Gao, Kang & Yuan, Yijun, 2022. "Spatiotemporal pattern assessment of China’s industrial green productivity and its spatial drivers: Evidence from city-level data over 2000–2017," Applied Energy, Elsevier, vol. 307(C).
    7. Zhong, Liang & Liu, Xiaosheng & Ao, Jianfeng, 2022. "Spatiotemporal dynamics evaluation of pixel-level gross domestic product, electric power consumption, and carbon emissions in countries along the belt and road," Energy, Elsevier, vol. 239(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kangjuan Lv & Yu Cheng & Yousen Wang, 2021. "Does regional innovation system efficiency facilitate energy-related carbon dioxide intensity reduction in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 789-813, January.
    2. Hu, Ting & Huang, Xin, 2019. "A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data," Applied Energy, Elsevier, vol. 240(C), pages 778-792.
    3. Shi, Kaifang & Chen, Yun & Li, Linyi & Huang, Chang, 2018. "Spatiotemporal variations of urban CO2 emissions in China: A multiscale perspective," Applied Energy, Elsevier, vol. 211(C), pages 218-229.
    4. Wang, Jiaxin & Lu, Feng, 2021. "Modeling the electricity consumption by combining land use types and landscape patterns with nighttime light imagery," Energy, Elsevier, vol. 234(C).
    5. Du, Mengbing & Ruan, Jianhui & Zhang, Li & Niu, Muchuan & Zhang, Zhe & Xia, Lang & Qian, Shuangyue & Chen, Chuchu, 2024. "China's local-level monthly residential electricity power consumption monitoring," Applied Energy, Elsevier, vol. 359(C).
    6. Li, Shuyi & Cheng, Liang & Liu, Xiaoqiang & Mao, Junya & Wu, Jie & Li, Manchun, 2019. "City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data," Energy, Elsevier, vol. 189(C).
    7. Kangjuan Lv & Anyu Yu & Yiwen Bian, 2017. "Regional energy efficiency and its determinants in China during 2001–2010: a slacks-based measure and spatial econometric analysis," Journal of Productivity Analysis, Springer, vol. 47(1), pages 65-81, February.
    8. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
    9. Wang, Shaobin & Liu, Haimeng & Pu, Haixia & Yang, Hao, 2020. "Spatial disparity and hierarchical cluster analysis of final energy consumption in China," Energy, Elsevier, vol. 197(C).
    10. Lu, Linlin & Weng, Qihao & Xie, Yanhua & Guo, Huadong & Li, Qingting, 2019. "An assessment of global electric power consumption using the Defense Meteorological Satellite Program-Operational Linescan System nighttime light imagery," Energy, Elsevier, vol. 189(C).
    11. Yulan Lv & Wei Chen & Jianquan Cheng, 2019. "Direct and Indirect Effects of Urbanization on Energy Intensity in Chinese Cities: A Regional Heterogeneity Analysis," Sustainability, MDPI, vol. 11(11), pages 1-20, June.
    12. Wang, Shaobin & Zhao, Chao & Liu, Hanbin & Tian, Xinglei, 2021. "Exploring the spatial spillover effects of low-grade coal consumption and influencing factors in China," Resources Policy, Elsevier, vol. 70(C).
    13. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    14. Shi, Kaifang & Yu, Bailang & Huang, Chang & Wu, Jianping & Sun, Xiufeng, 2018. "Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road," Energy, Elsevier, vol. 150(C), pages 847-859.
    15. Yongqing Nan & Qin Li & Jinxiang Yu & Haiya Cai & Qin Zhou, 2020. "Has the emissions intensity of industrial sulphur dioxide converged? New evidence from China’s prefectural cities with dynamic spatial panel models," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5337-5369, August.
    16. Yang Zhong & Aiwen Lin & Zhigao Zhou & Feiyan Chen, 2018. "Spatial Pattern Evolution and Optimization of Urban System in the Yangtze River Economic Belt, China, Based on DMSP-OLS Night Light Data," Sustainability, MDPI, vol. 10(10), pages 1-14, October.
    17. Jasiński, Tomasz, 2019. "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, vol. 179(C), pages 831-842.
    18. Jiang, Ben & Li, Yu & Rezgui, Yacine & Zhang, Chengyu & Wang, Peng & Zhao, Tianyi, 2024. "Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings," Energy, Elsevier, vol. 299(C).
    19. Shiwen Liu & Zhen Zhang & Junhua Yang & Wei Hu, 2022. "Exploring Increasing Urban Resident Electricity Consumption: The Spatial Spillover Effect of Resident Income," Energies, MDPI, vol. 15(12), pages 1-17, June.
    20. Erik Hille & Bernhard Lambernd & Aviral K. Tiwari, 2021. "Any Signs of Green Growth? A Spatial Panel Analysis of Regional Air Pollution in South Korea," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 80(4), pages 719-760, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221006009. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.