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

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  • 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
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    2. 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.
    3. 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).
    4. 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.
    5. 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).
    6. 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.
    7. 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).
    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).

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