IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v229y2021ics0360544221009270.html
   My bibliography  Save this article

Energy and flow demand analysis of domestic hot water in an apartment complex using a smart meter

Author

Listed:
  • Lee, Jae Yong
  • Yim, Taesu

Abstract

This study aims to reveal new perspectives on domestic hot water use regarding seasonal behavior, peak demand deviation with temporal metering resolution and time averaging, initially discarded characteristics, and the possibility of demand forecasting based on the previous outdoor temperature. To determine the use of domestic hot water in 918 households located at the study site, new smart meters and peripheral systems were used to collect sub-meter readings of energy/flow/discarded hot water demands and endpoint temperatures every 30 s. Collected data on individual households can provide quantitative information about seasonal changes, variations in hot water service quality among end-users, and end-user behaviors of initial use. Furthermore, statistical and machine learning analyses indicate the possibility of forecasting seasonal domestic hot water consumption through an understanding of the relationship between energy demand and previous outdoor temperatures. Through a clear understanding and with a proper use of the demand behavior as it pertains to domestic hot water, guidelines can be derived to enable the efficient use of domestic hot water by households as well as the efficient operation by heat suppliers.

Suggested Citation

  • Lee, Jae Yong & Yim, Taesu, 2021. "Energy and flow demand analysis of domestic hot water in an apartment complex using a smart meter," Energy, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:energy:v:229:y:2021:i:c:s0360544221009270
    DOI: 10.1016/j.energy.2021.120678
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221009270
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.120678?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. Emery, A.F. & Kippenhan, C.J., 2006. "A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards," Energy, Elsevier, vol. 31(5), pages 677-693.
    2. Protić, Milan & Shamshirband, Shahaboddin & Petković, Dalibor & Abbasi, Almas & Mat Kiah, Miss Laiha & Unar, Jawed Akhtar & Živković, Ljiljana & Raos, Miomir, 2015. "Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm," Energy, Elsevier, vol. 87(C), pages 343-351.
    3. Levinson, Arik & Niemann, Scott, 2004. "Energy use by apartment tenants when landlords pay for utilities," Resource and Energy Economics, Elsevier, vol. 26(1), pages 51-75, March.
    4. Fuentes, E. & Arce, L. & Salom, J., 2018. "A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1530-1547.
    5. Alessandro Burgio & Daniele Menniti & Nicola Sorrentino & Anna Pinnarelli & Zbigniew Leonowicz, 2020. "Influence and Impact of Data Averaging and Temporal Resolution on the Assessment of Energetic, Economic and Technical Issues of Hybrid Photovoltaic-Battery Systems," Energies, MDPI, vol. 13(2), pages 1-26, January.
    6. 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.
    7. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
    8. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
    9. Gans, Will & Alberini, Anna & Longo, Alberto, 2013. "Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland," Energy Economics, Elsevier, vol. 36(C), pages 729-743.
    10. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
    11. Bagge, Hans & Johansson, Dennis, 2011. "Measurements of household electricity and domestic hot water use in dwellings and the effect of different monitoring time resolution," Energy, Elsevier, vol. 36(5), pages 2943-2951.
    12. Kazutoshi Tsuda & Michinori Uwasu & Keishiro Hara & Yukari Fuchigami, 2017. "Approaches to induce behavioral changes with respect to electricity consumption," Journal of Environmental Studies and Sciences, Springer;Association of Environmental Studies and Sciences, vol. 7(1), pages 30-38, March.
    13. Schultz, P. Wesley & Estrada, Mica & Schmitt, Joseph & Sokoloski, Rebecca & Silva-Send, Nilmini, 2015. "Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms," Energy, Elsevier, vol. 90(P1), pages 351-358.
    14. Ivanko, Dmytro & Sørensen, Åse Lekang & Nord, Natasa, 2021. "Splitting measurements of the total heat demand in a hotel into domestic hot water and space heating heat use," Energy, Elsevier, vol. 219(C).
    15. Izadyar, Nima & Ghadamian, Hossein & Ong, Hwai Chyuan & moghadam, Zeinab & Tong, Chong Wen & Shamshirband, Shahaboddin, 2015. "Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption," Energy, Elsevier, vol. 93(P2), pages 1558-1567.
    16. Braas, Hagen & Jordan, Ulrike & Best, Isabelle & Orozaliev, Janybek & Vajen, Klaus, 2020. "District heating load profiles for domestic hot water preparation with realistic simultaneity using DHWcalc and TRNSYS," Energy, Elsevier, vol. 201(C).
    17. Shimoda, Yoshiyuki & Okamura, Tomo & Yamaguchi, Yohei & Yamaguchi, Yukio & Taniguchi, Ayako & Morikawa, Takao, 2010. "City-level energy and CO2 reduction effect by introducing new residential water heaters," Energy, Elsevier, vol. 35(12), pages 4880-4891.
    18. Best, Isabelle & Braas, Hagen & Orozaliev, Janybek & Jordan, Ulrike & Vajen, Klaus, 2020. "Systematic investigation of building energy efficiency standard and hot water preparation systems’ influence on the heat load profile of districts," Energy, Elsevier, vol. 197(C).
    19. Yu, Zhun (Jerry) & Haghighat, Fariborz & Fung, Benjamin C.M. & Morofsky, Edward & Yoshino, Hiroshi, 2011. "A methodology for identifying and improving occupant behavior in residential buildings," Energy, Elsevier, vol. 36(11), pages 6596-6608.
    20. Kempton, Willett, 1988. "Residential hot water: A behaviorally-driven system," Energy, Elsevier, vol. 13(1), pages 107-114.
    21. Lund, Henrik & Werner, Sven & Wiltshire, Robin & Svendsen, Svend & Thorsen, Jan Eric & Hvelplund, Frede & Mathiesen, Brian Vad, 2014. "4th Generation District Heating (4GDH)," Energy, Elsevier, vol. 68(C), pages 1-11.
    22. Al-Shammari, Eiman Tamah & Keivani, Afram & Shamshirband, Shahaboddin & Mostafaeipour, Ali & Yee, Por Lip & Petković, Dalibor & Ch, Sudheer, 2016. "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm," Energy, Elsevier, vol. 95(C), pages 266-273.
    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. Simone Ferrari & Milad Zoghi & Giancarlo Paganin & Giuliano Dall’O’, 2023. "A Practical Review to Support the Implementation of Smart Solutions within Neighbourhood Building Stock," Energies, MDPI, vol. 16(15), pages 1-35, July.
    2. Beata Piotrowska & Daniel Słyś, 2023. "Analysis of the Life Cycle Cost of a Heat Recovery System from Greywater Using a Vertical “Tube-in-Tube” Heat Exchanger: Case Study of Poland," Resources, MDPI, vol. 12(9), pages 1-17, August.
    3. Ahammed, Md. Tanvir & Khan, Imran, 2022. "Ensuring power quality and demand-side management through IoT-based smart meters in a developing country," Energy, Elsevier, vol. 250(C).
    4. Łukasz Amanowicz, 2021. "Peak Power of Heat Source for Domestic Hot Water Preparation (DHW) for Residential Estate in Poland as a Representative Case Study for the Climate of Central Europe," Energies, MDPI, vol. 14(23), pages 1-15, December.
    5. Yang, Zhaoming & Liu, Zhe & Zhou, Jing & Song, Chaofan & Xiang, Qi & He, Qian & Hu, Jingjing & Faber, Michael H. & Zio, Enrico & Li, Zhenlin & Su, Huai & Zhang, Jinjun, 2023. "A graph neural network (GNN) method for assigning gas calorific values to natural gas pipeline networks," Energy, Elsevier, vol. 278(C).
    6. Wojciech Rzeźnik & Ilona Rzeźnik & Paweł Hara, 2022. "Comparison of Real and Forecasted Domestic Hot Water Consumption and Demand for Heat Power in Multifamily Buildings, in Poland," Energies, MDPI, vol. 15(19), pages 1-17, September.
    7. Bartnicki, Grzegorz & Klimczak, Marcin & Ziembicki, Piotr, 2023. "Evaluation of the effects of optimization of gas boiler burner control by means of an innovative method of Fuel Input Factor," Energy, Elsevier, vol. 263(PD).
    8. Barnaś, Krzysztof & Jeleński, Tomasz & Nowak-Ocłoń, Marzena & Racoń-Leja, Kinga & Radziszewska-Zielina, Elżbieta & Szewczyk, Bartłomiej & Śladowski, Grzegorz & Toś, Cezary & Varbanov, Petar Sabev, 2023. "Algorithm for the comprehensive thermal retrofit of housing stock aided by renewable energy supply: A sustainable case for Krakow," Energy, Elsevier, vol. 263(PD).

    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. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    2. Xue, Puning & Zhou, Zhigang & Fang, Xiumu & Chen, Xin & Liu, Lin & Liu, Yaowen & Liu, Jing, 2017. "Fault detection and operation optimization in district heating substations based on data mining techniques," Applied Energy, Elsevier, vol. 205(C), pages 926-940.
    3. Xue, Puning & Jiang, Yi & Zhou, Zhigang & Chen, Xin & Fang, Xiumu & Liu, Jing, 2019. "Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms," Energy, Elsevier, vol. 188(C).
    4. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    5. Xue, Guixiang & Qi, Chengying & Li, Han & Kong, Xiangfei & Song, Jiancai, 2020. "Heating load prediction based on attention long short term memory: A case study of Xingtai," Energy, Elsevier, vol. 203(C).
    6. Piselli, Cristina & Pisello, Anna Laura, 2019. "Occupant behavior long-term continuous monitoring integrated to prediction models: Impact on office building energy performance," Energy, Elsevier, vol. 176(C), pages 667-681.
    7. Østergaard, Dorte Skaarup & Smith, Kevin Michael & Tunzi, Michele & Svendsen, Svend, 2022. "Low-temperature operation of heating systems to enable 4th generation district heating: A review," Energy, Elsevier, vol. 248(C).
    8. Strong, Derek Ryan, 2017. "The Early Diffusion of Smart Meters in the US Electric Power Industry," Thesis Commons 7zprk, Center for Open Science.
    9. Guo, Zhifeng & Zhou, Kaile & Zhang, Chi & Lu, Xinhui & Chen, Wen & Yang, Shanlin, 2018. "Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 399-412.
    10. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    11. Meireles, I. & Sousa, V. & Bleys, B. & Poncelet, B., 2022. "Domestic hot water consumption pattern: Relation with total water consumption and air temperature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    12. Gong, Mingju & Zhao, Yin & Sun, Jiawang & Han, Cuitian & Sun, Guannan & Yan, Bo, 2022. "Load forecasting of district heating system based on Informer," Energy, Elsevier, vol. 253(C).
    13. Plappally, A.K. & Lienhard V, J.H., 2012. "Energy requirements for water production, treatment, end use, reclamation, and disposal," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4818-4848.
    14. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    15. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    16. Maltais, Louis-Gabriel & Gosselin, Louis, 2021. "Predictability analysis of domestic hot water consumption with neural networks: From single units to large residential buildings," Energy, Elsevier, vol. 229(C).
    17. Fettermann, Diego Castro & Cavalcante, Caroline Gobbo Sá & Ayala, Néstor Fabián & Avalone, Marianne Costa, 2020. "Configuration of a smart meter for Brazilian customers," Energy Policy, Elsevier, vol. 139(C).
    18. Shakeel, Asim & Chong, Daotong & Wang, Jinshi, 2023. "Load forecasting of district heating system based on improved FB-Prophet model," Energy, Elsevier, vol. 278(C).
    19. Chanuk Lee & Dong Eun Jung & Donghoon Lee & Kee Han Kim & Sung Lok Do, 2021. "Prediction Performance Analysis of Artificial Neural Network Model by Input Variable Combination for Residential Heating Loads," Energies, MDPI, vol. 14(3), pages 1-19, February.
    20. Gu, Jihao & Wang, Jin & Qi, Chengying & Min, Chunhua & Sundén, Bengt, 2018. "Medium-term heat load prediction for an existing residential building based on a wireless on-off control system," Energy, Elsevier, vol. 152(C), pages 709-718.

    More about this item

    Statistics

    Access and download statistics

    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:229:y:2021:i:c:s0360544221009270. 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.