journal article·2024-10-29Open access

Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building

Junghoon Wee, Yeonghun Hong, Young Tae Chae

Applied Sciences· Volume 14· Issue 21

Abstract

This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort.
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Research context

OpenAlex topics: Building Energy and Comfort Optimization · Urban Heat Island Mitigation · Infection Control and Ventilation

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Architectural Engineering, College of Engineering, Gachon University, 1332, Seongnam-daero, Sujeong-gu, Seongnam-si 13306, Gyeonggi-do, Republic of Korea

Department of Computer Science, College of IT convergence, Gachon University, 1332, Seongnam-daero, Sujeong-gu, Seongnam-si 13306, Gyeonggi-do, Republic of Korea

Funding

Korea government (MSIT) · Korea Institute of Energy Technology Evaluation and Planning (KETEP) · Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea

References (44)

Show source-provided references
1. A review on buildings energy information: Trends, end-uses, fuels and drivers10.1016/j.egyr.2021.11.280 ↗
2. US EIA 2012: Annual Energy Outlook 2012 Early Release, Jan. 2012, Summary Reference Case.
3. Present and future energy consumption of buildings: Challenges and opportunities towards decarbonization
4. IEA (2021). Shares of Residential Energy Consumption by end Use in Selected IEA Countries, 2019, IEA.
5. Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run10.1016/j.apenergy.2018.03.108 ↗
6. A study on influential factors of occupant window-opening behavior in an office building in China10.1016/j.buildenv.2018.02.008 ↗
7. A data-mining approach to discover patterns of window opening and closing behavior in offices10.1016/j.buildenv.2014.10.021 ↗
8. Mun, S.H., Kwak, Y., and Huh, J.H. (2021). Influence of complex occupant behavior models on cooling energy usage analysis. Sustainability, 13.10.3390/su13031243 ↗
9. Probability of occupant operation of windows during transition seasons in office buildings10.1016/j.renene.2014.05.065 ↗
10. Survey of occupant behaviour and control of indoor environment in Danish dwellings10.1016/j.enbuild.2008.07.004 ↗
11. Mori, H., Kubota, T., Antaryama, I.G.N., and Ekasiwi, S.N.N. (2020). Analysis of window-opening patterns and air conditioning usage of urban residences in tropical southeast Asia. Sustainability, 12.10.3390/su122410650 ↗
12. Nguyen, T.H., Ionescu, A., Ramalho, O., and Gehin, E. (2021). Predicting the Window Opening State in an Office to Improve Indoor Air Quality. Eng. Proc., 5.10.3390/engproc2021005024 ↗
13. Performance evaluation of an agent-based occupancy simulation model10.1016/j.buildenv.2017.01.015 ↗
14. Sourav, M.S.G., Yavari, E., Gao, X., Maskrey, J., Zheng, Y., Lubecke, V.M., and Boric-Lubecke, O. (2024). Occupancy Estimation from Blurred Video: A Multifaceted Approach with Privacy Consideration. Sensors, 24.10.3390/s24123739 ↗
15. Modeling regular occupancy in commercial buildings using stochastic models10.1016/j.enbuild.2015.06.009 ↗
16. Qaisar, I., Sun, K., Zhao, Q., Xing, T., and Yan, H. (2023). Multi-Sensor-Based Occupancy Prediction in a Multi-Zone Office Building with Transformer. Buildings, 13.10.3390/buildings13082002 ↗
17. Determining the optimal occupancy density for reducing the energy consumption of public office buildings: A statistical approach10.1016/j.buildenv.2017.11.010 ↗
18. Perra, C., Kumar, A., Losito, M., Pirino, P., Moradpour, M., and Gatto, G. (2021). Monitoring Indoor People Presence in Buildings Using Low-Cost Infrared Sensor Array in Doorways. Sensors, 21.10.3390/s21124062 ↗
19. Bäcklund, K., Molinari, M., Lundqvist, P., and Palm, B. (2023). Building Occupants, Their Behavior and the Resulting Impact on Energy Use in Campus Buildings: A Literature Review with Focus on Smart Building Systems. Energies, 16.10.3390/en16176104 ↗
20. Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants’ behavior10.1016/j.energy.2013.03.086 ↗
21. Data driven occupancy information for energy simulation and energy use assessment in residential buildings10.1016/j.energy.2020.119539 ↗
22. Najeh, H., Lohr, C., and Leduc, B. (2024). Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study. Appl. Sci., 14.10.3390/app14062377 ↗
23. Impact of building characteristics and occupants’ behaviour on the electricity consumption of households in Abu Dhabi (UAE)10.1016/j.enbuild.2017.07.019 ↗
24. A stochastic model of integrating occupant behaviour into energy simulation with respect to actual energy consumption in high-rise apartment buildings10.1016/j.enbuild.2016.03.037 ↗
25. Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments10.1016/j.apenergy.2017.06.079 ↗
26. The application of thermal comfort control based on Smart House System of IoT10.1016/j.measurement.2019.106997 ↗
27. Gharamohammadi, A., Dabak, A.G., Yang, Z., Khajepour, A., and Shaker, G. (2024). Volume-Based Occupancy Detection for In-Cabin Applications by Millimeter Wave Radar. Remote Sens., 16.10.3390/rs16163068 ↗
28. Babiuch, M., Foltỳnek, P., and Smutnỳ, P. (2019, January 26–29). Using the ESP32 microcontroller for data processing. Proceedings of the 2019 20th International Carpathian Control Conference (ICCC), Krakow-Wieliczka, Poland.10.1109/CarpathianCC.2019.8765944 ↗
29. Burimas, R., Horanont, T., Thapa, A., and Lamichhane, B.R. (2024). Monitoring the Sleep Respiratory Rate with Low-Cost Microcontroller Wi-Fi in a Controlled Environment. Appl. Sci., 14.10.3390/app14156458 ↗
30. Marković, M. (2024, August 10). Why Is ESP the Most Used IoT Connectivity MCU. Available online: https://www.byte-lab.com/why-is-esp-the-most-used-iot-connectivity-mcu/.
31. Indirect calorimetry on the metabolic rate of sitting, standing and walking office activities10.1016/j.buildenv.2018.09.011 ↗
32. A comparison of open-source home automation systems10.1109/ACCESS.2021.3136025 ↗
33. Validation of methods for determination of metabolic rate in the Edholm scale and ISO 899610.1080/10803548.2001.11076482 ↗
34. A practical system of units for the description of the heat exchange of man with his environment10.1126/science.94.2445.428 ↗
35. (2005). Ergonomics of the Thermal Environment-Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria (Standard No. ISO 7730:2005).
36. (2013). Thermal Environmental Conditions for Human Occupancy (Standard No. ANSI/ASHRAE/IES Standard 55-2013).
37. Interaction of clothing and thermoregulation10.1159/000068802 ↗
38. A database of clothing overall and local insulation and prediction models for estimating ensembles’ insulation10.1016/j.buildenv.2021.108418 ↗
39. A study on the thermal comfort in sleeping environments in the subtropics—developing a thermal comfort model for sleeping environments10.1016/j.buildenv.2006.11.026 ↗
40. A review on modeling and simulation of building energy systems10.1016/j.rser.2015.12.040 ↗
41. Functional mock-up unit for co-simulation import in EnergyPlus10.1080/19401493.2013.808265 ↗
42. Detailed cross comparison of building energy simulation tools results using a reference office building as a case study10.1016/j.enbuild.2021.111260 ↗
43. Wilson, E. (2023). Multifamily Building Stock Modeling: Cooperative Research and Development Final Report, CRADA Number CRD-17-00701, National Renewable Energy Laboratory (NREL). (No. NREL/TP-5500-73294).10.2172/1496589 ↗
44. Fanger, P.O. (1970). Thermal Comfort. Analysis and Applications in Environmental Engineering, Danish Technical Press.