IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v310y2022ics0306261922000265.html
   My bibliography  Save this article

Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona

Author

Listed:
  • Ku, Arthur Lin
  • Qiu, Yueming (Lucy)
  • Lou, Jiehong
  • Nock, Destenie
  • Xing, Bo

Abstract

The transition to remote work brings uncertainty to the future power consumption pattern. The COVID mandates in 2020 have accelerated the transition to remote work, generating major uncertainty regarding how residential power consumption patterns will shift. Understanding these shifts is vital for regional operators who will need to implement long-term planning strategies if companies continue to adopt remote work practices. Additionally, if new COVID variants prompt extended stay-at-home mandates, the resulting behavior shifts will decide the optimal combination of power generation in a region. Our study examines changes in hourly residential power consumption patterns resulting from COVID mandates in Arizona. We estimate how the COVID mandates and subsequent remote work practices could change the power consumption patterns using individual-consumer-level hourly power consumption data for 6,309 consumers and a machine learning framework. We also simulate how the hourly power consumption pattern will change with increasing penetration of remote work under winter and summer temperature settings. We then use our simulations to test the policy effectiveness of changing time-of-use (TOU) rates. Our results show that COVID mandates likely increase the power consumption in the afternoon by 13%, and can change the power consumption pattern in winter from a two-peaked shape to a one-peaked shape. Furthermore, we show that the residents' income, race, and house size are significantly correlated with the changes in power consumption, and the correlation is not linear. We find that, by increasing the peak hour prices and decreasing the off-peak hour prices by 10% of the TOU pricing, the peak electricity demand could be reduced by 10%. Our results show under the new remote work era: (1) the need for modifying previous energy generation combination planning due to changing peak demand hours; (2) equity concerns regarding TOU pricing and the inability of vulnerable groups to shift electricity consumption; (3) the ability of governments and utilities to lower the maximum load of power consumption by modifying the TOU rates.

Suggested Citation

  • Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
  • Handle: RePEc:eee:appene:v:310:y:2022:i:c:s0306261922000265
    DOI: 10.1016/j.apenergy.2022.118539
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922000265
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.118539?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. Brown, Marilyn A. & Chapman, Oliver, 2021. "The size, causes, and equity implications of the demand-response gap," Energy Policy, Elsevier, vol. 158(C).
    2. Krarti, Moncef & Aldubyan, Mohammad, 2021. "Review analysis of COVID-19 impact on electricity demand for residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Trevor Memmott & Sanya Carley & Michelle Graff & David M. Konisky, 2021. "Sociodemographic disparities in energy insecurity among low-income households before and during the COVID-19 pandemic," Nature Energy, Nature, vol. 6(2), pages 186-193, February.
    5. Sanquist, Thomas F. & Orr, Heather & Shui, Bin & Bittner, Alvah C., 2012. "Lifestyle factors in U.S. residential electricity consumption," Energy Policy, Elsevier, vol. 42(C), pages 354-364.
    6. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    7. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    8. Wayne B. Gray & Jay P. Shimshack, 2011. "The Effectiveness of Environmental Monitoring and Enforcement: A Review of the Empirical Evidence," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 5(1), pages 3-24, Winter.
    9. Yueming Qiu & Loren Kirkeide & Yi David Wang, 2018. "Effects of Voluntary Time-of-Use Pricing on Summer Electricity Usage of Business Customers," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 69(2), pages 417-440, February.
    10. Burleyson, Casey D. & Rahman, Aowabin & Rice, Jennie S. & Smith, Amanda D. & Voisin, Nathalie, 2021. "Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States," Applied Energy, Elsevier, vol. 304(C).
    11. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    12. Agbim, Chinelo & Araya, Felipe & Faust, Kasey M. & Harmon, Dana, 2020. "Subjective versus objective energy burden: A look at drivers of different metrics and regional variation of energy poor populations," Energy Policy, Elsevier, vol. 144(C).
    13. Li, Chuan & Tao, Ying & Ao, Wengang & Yang, Shuai & Bai, Yun, 2018. "Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition," Energy, Elsevier, vol. 165(PB), pages 1220-1227.
    14. Healy, John D. & Clinch, J. Peter, 2002. "Fuel poverty, thermal comfort and occupancy: results of a national household-survey in Ireland," Applied Energy, Elsevier, vol. 73(3-4), pages 329-343, November.
    15. He, Feifei & Zhou, Jianzhong & Mo, Li & Feng, Kuaile & Liu, Guangbiao & He, Zhongzheng, 2020. "Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest," Applied Energy, Elsevier, vol. 262(C).
    16. Wang, Endong, 2017. "Decomposing core energy factor structure of U.S. residential buildings through principal component analysis with variable clustering on high-dimensional mixed data," Applied Energy, Elsevier, vol. 203(C), pages 858-873.
    17. Stelmach, Greg & Zanocco, Chad & Flora, June & Rajagopal, Ram & Boudet, Hilary S., 2020. "Exploring household energy rules and activities during peak demand to better determine potential responsiveness to time-of-use pricing," Energy Policy, Elsevier, vol. 144(C).
    18. Dominic J. Bednar & Tony G. Reames, 2020. "Recognition of and response to energy poverty in the United States," Nature Energy, Nature, vol. 5(6), pages 432-439, June.
    19. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    20. Muchlinski, David & Siroky, David & He, Jingrui & Kocher, Matthew, 2016. "Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data," Political Analysis, Cambridge University Press, vol. 24(1), pages 87-103, January.
    21. Guoyi Zhang & Yan Lu, 2012. "Bias-corrected random forests in regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(1), pages 151-160, March.
    22. Bella, Giovanni & Massidda, Carla & Mattana, Paolo, 2014. "The relationship among CO2 emissions, electricity power consumption and GDP in OECD countries," Journal of Policy Modeling, Elsevier, vol. 36(6), pages 970-985.
    23. Werth, Annette & Gravino, Pietro & Prevedello, Giulio, 2021. "Impact analysis of COVID-19 responses on energy grid dynamics in Europe," Applied Energy, Elsevier, vol. 281(C).
    24. Ruan, Guangchun & Wu, Jiahan & Zhong, Haiwang & Xia, Qing & Xie, Le, 2021. "Quantitative assessment of U.S. bulk power systems and market operations during the COVID-19 pandemic," Applied Energy, Elsevier, vol. 286(C).
    25. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
    26. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
    27. Ueno, Tsuyoshi & Sano, Fuminori & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data," Applied Energy, Elsevier, vol. 83(2), pages 166-183, February.
    28. Linh T. T. Ho & Laurent Dubus & Matteo De Felice & Alberto Troccoli, 2020. "Reconstruction of Multidecadal Country-Aggregated Hydro Power Generation in Europe Based on a Random Forest Model," Energies, MDPI, vol. 13(7), pages 1-17, April.
    29. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
    30. Saska Petrova & Michael Gentile & Ilkka Henrik Mäkinen & Stefan Bouzarovski, 2013. "Perceptions of Thermal Comfort and Housing Quality: Exploring the Microgeographies of Energy Poverty in Stakhanov, Ukraine," Environment and Planning A, , vol. 45(5), pages 1240-1257, May.
    31. Yang, Ting & Ren, Minglun & Zhou, Kaile, 2018. "Identifying household electricity consumption patterns: A case study of Kunshan, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 861-868.
    32. Sławomir Bielecki & Tadeusz Skoczkowski & Lidia Sobczak & Janusz Buchoski & Łukasz Maciąg & Piotr Dukat, 2021. "Impact of the Lockdown during the COVID-19 Pandemic on Electricity Use by Residential Users," Energies, MDPI, vol. 14(4), pages 1-32, February.
    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. Nicolae-Marius Jula & Diana-Mihaela Jula & Bogdan Oancea & Răzvan-Mihail Papuc & Dorin Jula, 2023. "Changes in the Pattern of Weekdays Electricity Real Consumption during the COVID-19 Crisis," Energies, MDPI, vol. 16(10), pages 1-20, May.
    2. Joseph Crawford, 2022. "Working from Home, Telework, and Psychological Wellbeing? A Systematic Review," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    3. Garcia-Rendon, John & Rey Londoño, Felipe & Arango Restrepo, Luis José & Bohorquez Correa, Santiago, 2023. "Sectoral analysis of electricity consumption during the COVID-19 pandemic: Evidence for unregulated and regulated markets in Colombia," Energy, Elsevier, vol. 268(C).

    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. Chen, Xiao & Zanocco, Chad & Flora, June & Rajagopal, Ram, 2022. "Constructing dynamic residential energy lifestyles using Latent Dirichlet Allocation," Applied Energy, Elsevier, vol. 318(C).
    2. Indre Siksnelyte-Butkiene, 2021. "Impact of the COVID-19 Pandemic to the Sustainability of the Energy Sector," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    3. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    4. Joe F. Bozeman & Shauhrat S. Chopra & Philip James & Sajjad Muhammad & Hua Cai & Kangkang Tong & Maya Carrasquillo & Harold Rickenbacker & Destenie Nock & Weslynne Ashton & Oliver Heidrich & Sybil Der, 2023. "Three research priorities for just and sustainable urban systems: Now is the time to refocus," Journal of Industrial Ecology, Yale University, vol. 27(2), pages 382-394, April.
    5. Costa, Vinicius B.F. & Pereira, Lígia C. & Andrade, Jorge V.B. & Bonatto, Benedito D., 2022. "Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model," Applied Energy, Elsevier, vol. 313(C).
    6. Jasiński, Tomasz, 2022. "A new approach to modeling cycles with summer and winter demand peaks as input variables for deep neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    7. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    9. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    10. M. A. Hannan & M. S. Abd Rahman & Ali Q. Al-Shetwi & R. A. Begum & Pin Jern Ker & M. Mansor & M. S. Mia & M. J. Hossain & Z. Y. Dong & T. M. I. Mahlia, 2022. "Impact Assessment of COVID-19 Severity on Environment, Economy and Society towards Affecting Sustainable Development Goals," Sustainability, MDPI, vol. 14(23), pages 1-23, November.
    11. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
    12. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    13. Ye, Yuxiang & Koch, Steven F., 2021. "Measuring energy poverty in South Africa based on household required energy consumption," Energy Economics, Elsevier, vol. 103(C).
    14. Qi Li & Wei Long, 2018. "Do parole abolition and Truth-in-Sentencing deter violent crimes in Virginia?," Empirical Economics, Springer, vol. 55(4), pages 2027-2045, December.
    15. Burleyson, Casey D. & Rahman, Aowabin & Rice, Jennie S. & Smith, Amanda D. & Voisin, Nathalie, 2021. "Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States," Applied Energy, Elsevier, vol. 304(C).
    16. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    17. Guido W. Imbens, 2022. "Causality in Econometrics: Choice vs Chance," Econometrica, Econometric Society, vol. 90(6), pages 2541-2566, November.
    18. Jason Poulos & Andrea Albanese & Andrea Mercatanti & Fan Li, 2021. "Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment," Papers 2106.00788, arXiv.org.
    19. Tomasz Wołowiec & Iuliia Myroshnychenko & Ihor Vakulenko & Sylwester Bogacki & Anna Maria Wiśniewska & Svitlana Kolosok & Vitaliy Yunger, 2022. "International Impact of COVID-19 on Energy Economics and Environmental Pollution: A Scoping Review," Energies, MDPI, vol. 15(22), pages 1-26, November.
    20. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.

    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:appene:v:310:y:2022:i:c:s0306261922000265. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.