IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipcs0306261924019664.html
   My bibliography  Save this article

Revealing spatial and temporal patterns of residential cooling in Southern California through combined estimates of AC ownership and use

Author

Listed:
  • Peplinski, McKenna
  • Mayes, Stepp
  • Sanders, Kelly T.

Abstract

Air conditioning (AC) is an important tool for combatting the adverse health effects of heat, but its use can also drive surges of high demand for electricity. To better understand these effects, there is a need for non-intrusive methods of estimating AC access and operation. In this study, we use a novel methodology to identify residential AC ownership rates using smart meter data from 200,000 customers in Southern California, and find that 79 % of all customers in the region have AC. In contrast to previous methods, we classify AC ownership using hourly, rather than daily, electricity consumption records and directly account for the potential presence of electric heating. We then adapt and apply an algorithm to determine in which hours these households operate their AC. We estimate that the average customer runs their AC during 8.3 % of all hours in the two-year study period, but census-tract level averages range from 1 to 23 % of all hours. Lastly, we combine our estimates of AC ownership and use to analyze cooling behavior spatially and temporally, and are able to identify pockets of high cooling demand, areas lacking in access or the ability to use their AC, and potential targets for cooling-related DR programs.

Suggested Citation

  • Peplinski, McKenna & Mayes, Stepp & Sanders, Kelly T., 2025. "Revealing spatial and temporal patterns of residential cooling in Southern California through combined estimates of AC ownership and use," Applied Energy, Elsevier, vol. 377(PC).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924019664
    DOI: 10.1016/j.apenergy.2024.124583
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924019664
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124583?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. Hahn, Heiko & Meyer-Nieberg, Silja & Pickl, Stefan, 2009. "Electric load forecasting methods: Tools for decision making," European Journal of Operational Research, Elsevier, vol. 199(3), pages 902-907, December.
    2. Zhu, Mengshu & Huang, Ying & Wang, Si-Nuo & Zheng, Xinye & Wei, Chu, 2023. "Characteristics and patterns of residential energy consumption for space cooling in China: Evidence from appliance-level data," Energy, Elsevier, vol. 265(C).
    3. Waite, Michael & Cohen, Elliot & Torbey, Henri & Piccirilli, Michael & Tian, Yu & Modi, Vijay, 2017. "Global trends in urban electricity demands for cooling and heating," Energy, Elsevier, vol. 127(C), pages 786-802.
    4. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
    5. Alan Barreca & Karen Clay & Olivier Deschenes & Michael Greenstone & Joseph S. Shapiro, 2016. "Adapting to Climate Change: The Remarkable Decline in the US Temperature-Mortality Relationship over the Twentieth Century," Journal of Political Economy, University of Chicago Press, vol. 124(1), pages 105-159.
    6. Chua, K.J. & Chou, S.K., 2010. "Energy performance of residential buildings in Singapore," Energy, Elsevier, vol. 35(2), pages 667-678.
    7. Elmallah, Salma & Crespo Montañés, Cristina & Callaway, Duncan, 2024. "Who heats and cools? Access to residential heating and cooling in Northern California and implications for energy transitions," Energy Policy, Elsevier, vol. 191(C).
    8. Dyson, Mark E.H. & Borgeson, Samuel D. & Tabone, Michaelangelo D. & Callaway, Duncan S., 2014. "Using smart meter data to estimate demand response potential, with application to solar energy integration," Energy Policy, Elsevier, vol. 73(C), pages 607-619.
    9. Westermann, Paul & Deb, Chirag & Schlueter, Arno & Evins, Ralph, 2020. "Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data," Applied Energy, Elsevier, vol. 264(C).
    Full references (including those not matched with items on IDEAS)

    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. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
    2. Mishra, Kakuli & Basu, Srinka & Maulik, Ujjwal, 2022. "Load profile mining using directed weighted graphs with application towards demand response management," Applied Energy, Elsevier, vol. 311(C).
    3. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).
    4. Marwan, Marwan, 2020. "The impact of probability of electricity price spike and outside temperature to define total expected cost for air conditioning," Energy, Elsevier, vol. 195(C).
    5. Alexander C. Abajian & Tamma Carleton & Kyle C. Meng & Olivier Deschênes, 2025. "Quantifying the global climate feedback from energy-based adaptation," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    6. Xi Chen & Chih Ming Tan & Xiaobo Zhang & Xin Zhang, 2020. "The effects of prenatal exposure to temperature extremes on birth outcomes: the case of China," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1263-1302, October.
    7. Hsing-Hsiang Huang & Michael R. Moore, 2018. "Farming under Weather Risk: Adaptation, Moral Hazard, and Selection on Moral Hazard," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 77-124, National Bureau of Economic Research, Inc.
    8. Huai Deng & Huan Wu & Hui Xu, 2025. "Social cost of carbon under endogenous social adaptation," Climatic Change, Springer, vol. 178(4), pages 1-20, April.
    9. Zhang, Shaohui & Guo, Qinxin & Smyth, Russell & Yao, Yao, 2022. "Extreme temperatures and residential electricity consumption: Evidence from Chinese households," Energy Economics, Elsevier, vol. 107(C).
    10. Wang, Meng & Zhang, Shiying, 2024. "High temperatures and traffic accident crimes: Evidence from more than 470,000 offenses in China," Economics & Human Biology, Elsevier, vol. 55(C).
    11. Cristina Cattaneo & Emanuele Massetti, 2019. "Does Harmful Climate Increase Or Decrease Migration? Evidence From Rural Households In Nigeria," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 10(04), pages 1-36, November.
    12. Antoine Dechezleprêtre & Adrien Fabre & Tobias Kruse & Bluebery Planterose & Ana Sanchez Chico & Stefanie Stantcheva, 2025. "Fighting Climate Change: International Attitudes toward Climate Policies," American Economic Review, American Economic Association, vol. 115(4), pages 1258-1300, April.
    13. Zhang, Yuyang & Ma, Wenke & Du, Pengcheng & Li, Shaoting & Gao, Ke & Wang, Yuxuan & Liu, Yifei & Zhang, Bo & Yu, Dingyi & Zhang, Jingyi & Li, Yan, 2024. "Powering the future: Unraveling residential building characteristics for accurate prediction of total electricity consumption during summer heat," Applied Energy, Elsevier, vol. 376(PA).
    14. Kalkuhl, Matthias & Wenz, Leonie, 2020. "The impact of climate conditions on economic production. Evidence from a global panel of regions," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    15. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    16. Paramita Sinha & Martha Caulkins & Maureen Cropper, 2018. "Do Discrete Choice Approaches to Valuing Urban Amenities Yield Different Results Than Hedonic Models?," NCEE Working Paper Series 201804, National Center for Environmental Economics, U.S. Environmental Protection Agency.
    17. Fritz, Manuela, 2021. "Temperature and non-communicable diseases: Evidence from Indonesia's primary health care system," Passauer Diskussionspapiere, Volkswirtschaftliche Reihe V-84-21, University of Passau, Faculty of Business and Economics.
    18. Yan Chen & Xiaohong Chen & Hongshan Ai & Xiaoqing Tan, 2022. "Temperature and Migration Intention: Evidence from the Unified National Graduate Entrance Examination in China," IJERPH, MDPI, vol. 19(16), pages 1-23, August.
    19. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
    20. Stefano Giglio & Bryan Kelly & Johannes Stroebel, 2021. "Climate Finance," Annual Review of Financial Economics, Annual Reviews, vol. 13(1), pages 15-36, November.

    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:377:y:2025:i:pc:s0306261924019664. 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.