IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224038180.html
   My bibliography  Save this article

Experiment and prediction analysis of thermal energy storage for heat load balancing in domestic hot water system

Author

Listed:
  • Ji, Hyung-Yong
  • Kang, Chaedong
  • Park, Dongho

Abstract

This paper presents the efficient process of thermal energy storage (TES) operation for heat load balancing in the domestic hot water (DHW) systems of district heating by the field experiments and prediction model. First, the TES system was developed with on-site customization for the apartment complex located in Suwon City, South Korea, and heat charge and discharge experiments were conducted hourly to reduce peak loads. The peak load balancing effect was effectively verified. Alongside the TES experiments, the heat amounts and patterns of DHW consumption of residents were monitored over a year. Then, using this monitored DHW heat consumption data, the prediction model of the deep neural network was developed and analyzed. In the prediction model, the monitored DHW consumption data served as the dependent variable, and various weather data and social factors were incorporated as independent variables. The dataset was segmented on an hourly basis, and time variables were categorized to predict DHW consumption values for each specific time. The TES system played a role in reducing peak load by approximately 25–40 % on an hourly basis during heat discharge operations, and the results of heat charge and discharge experiments of the TES system demonstrated a reduction in the dispersion of hourly DHW consumption by approximately 41.7 %, indicating the load balancing effect due to TES operation. Furthermore, the dispersion reduction effect applied with the predictive results showed a 6.8 % decrease compared to the condition with TES operations, and a 45.6 % decrease compared to the condition without TES operation. The prediction model effectively distinguished between heat charge and discharge times, enabling the effective application of the prediction model to the hourly charge and discharge process of TES operation over a day.

Suggested Citation

  • Ji, Hyung-Yong & Kang, Chaedong & Park, Dongho, 2024. "Experiment and prediction analysis of thermal energy storage for heat load balancing in domestic hot water system," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224038180
    DOI: 10.1016/j.energy.2024.134040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224038180
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.134040?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. Cui, Mianshan, 2022. "District heating load prediction algorithm based on bidirectional long short-term memory network model," Energy, Elsevier, vol. 254(PA).
    2. Kusiak, Andrew & Li, Mingyang & Zhang, Zijun, 2010. "A data-driven approach for steam load prediction in buildings," Applied Energy, Elsevier, vol. 87(3), pages 925-933, March.
    3. Guelpa, Elisa & Marincioni, Ludovica & Capone, Martina & Deputato, Stefania & Verda, Vittorio, 2019. "Thermal load prediction in district heating systems," Energy, Elsevier, vol. 176(C), pages 693-703.
    4. Pinson, P. & Nielsen, T.S. & Nielsen, H.Aa. & Poulsen, N.K. & Madsen, H., 2009. "Temperature prediction at critical points in district heating systems," European Journal of Operational Research, Elsevier, vol. 194(1), pages 163-176, April.
    5. Yuan, Jianjuan & Wang, Chendong & Zhou, Zhihua, 2019. "Study on refined control and prediction model of district heating station based on support vector machine," Energy, Elsevier, vol. 189(C).
    6. Shi, Xingping & He, Qing & Lu, Chang & Wang, Tingting & Cui, Shuangshuang & Du, Dongmei, 2023. "Variable load modes and operation characteristics of closed Brayton cycle pumped thermal electricity storage system with liquid-phase storage," Renewable Energy, Elsevier, vol. 203(C), pages 715-730.
    7. Jae-Ki Byun & Young-Don Choi & Jong-Keun Shin & Myung-Ho Park & Dong-Kurl Kwak, 2012. "Study on the Development of an Optimal Heat Supply Control Algorithm for Group Energy Apartment Buildings According to the Variation of Outdoor Air Temperature," Energies, MDPI, vol. 5(5), pages 1-19, May.
    8. He, Ke-Lun & Zhao, Tian & Ma, Huan & Chen, Qun, 2023. "Optimal operation of integrated power and thermal systems for flexibility improvement based on evaluation and utilization of heat storage in district heating systems," Energy, Elsevier, vol. 274(C).
    9. 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.
    10. Jin, Wencheng & Atkinson, Trevor A. & Doughty, Christine & Neupane, Ghanashyam & Spycher, Nicolas & McLing, Travis L. & Dobson, Patrick F. & Smith, Robert & Podgorney, Robert, 2022. "Machine-learning-assisted high-temperature reservoir thermal energy storage optimization," Renewable Energy, Elsevier, vol. 197(C), pages 384-397.
    11. Dolna, Oktawia, 2021. "Operation of a ground thermal energy storage supplied by different sources in a low-temperature district heating network," Renewable Energy, Elsevier, vol. 180(C), pages 586-604.
    12. Runge, Jason & Saloux, Etienne, 2023. "A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system," Energy, Elsevier, vol. 269(C).
    13. Dahl, Magnus & Brun, Adam & Andresen, Gorm B., 2017. "Using ensemble weather predictions in district heating operation and load forecasting," Applied Energy, Elsevier, vol. 193(C), pages 455-465.
    14. Noussan, Michel & Jarre, Matteo & Poggio, Alberto, 2017. "Real operation data analysis on district heating load patterns," Energy, Elsevier, vol. 129(C), pages 70-78.
    15. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    16. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    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. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
    2. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    3. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    4. Yuan, Jianjuan & Zhou, Zhihua & Huang, Ke & Han, Zhao & Wang, Chendong & Lu, Shilei, 2021. "Analysis and evaluation of the operation data for achieving an on-demand heating consumption prediction model of district heating substation," Energy, Elsevier, vol. 214(C).
    5. Zhong, Wei & Feng, Encheng & Lin, Xiaojie & Xie, Jinfang, 2022. "Research on data-driven operation control of secondary loop of district heating system," Energy, Elsevier, vol. 239(PB).
    6. Vogler–Finck, P.J.C. & Bacher, P. & Madsen, H., 2017. "Online short-term forecast of greenhouse heat load using a weather forecast service," Applied Energy, Elsevier, vol. 205(C), pages 1298-1310.
    7. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
    8. Yuan, Jianjuan & Zhou, Zhihua & Tang, Huajie & Wang, Chendong & Lu, Shilei & Han, Zhao & Zhang, Ji & Sheng, Ying, 2020. "Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system," Energy, Elsevier, vol. 199(C).
    9. 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).
    10. Liu, Guoqiang & Zhou, Xuan & Yan, Junwei & Yan, Gang, 2021. "A temperature and time-sharing dynamic control approach for space heating of buildings in district heating system," Energy, Elsevier, vol. 221(C).
    11. Chicherin, Stanislav, 2020. "Methodology for analyzing operation data for optimum district heating (DH) system design: Ten-year data of Omsk, Russia," Energy, Elsevier, vol. 211(C).
    12. Yuan, Jianjuan & Huang, Ke & Han, Zhao & Zhou, Zhihua & Lu, Shilei, 2021. "A new feedback predictive model for improving the operation efficiency of heating station based on indoor temperature," Energy, Elsevier, vol. 222(C).
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    17. 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).
    18. Salman Siddiqui & Mark Barrett & John Macadam, 2021. "A High Resolution Spatiotemporal Urban Heat Load Model for GB," Energies, MDPI, vol. 14(14), pages 1-28, July.
    19. Bergsteinsson, Hjörleifur G. & Møller, Jan Kloppenborg & Nystrup, Peter & Pálsson, Ólafur Pétur & Guericke, Daniela & Madsen, Henrik, 2021. "Heat load forecasting using adaptive temporal hierarchies," Applied Energy, Elsevier, vol. 292(C).
    20. Yang, Miao & Ding, Tao & Chang, Xinyue & Xue, Yixun & Ge, Huaichang & Jia, Wenhao & Du, Sijun & Zhang, Hongji, 2024. "Analysis of equivalent energy storage for integrated electricity-heat system," Energy, Elsevier, vol. 303(C).

    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:313:y:2024:i:c:s0360544224038180. 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.