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

Hyung-Yong Ji, Chaedong Kang and Dongho Park

Energy, 2024, vol. 313, issue C

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.

Keywords: Thermal storage system; District hot water; Heat load balance; Heat demand prediction; Machine learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:313:y:2024:i:c:s0360544224038180

DOI: 10.1016/j.energy.2024.134040

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