Journal Home > Volume 14 , Issue 3
Building Simulation 2021, 14(3): 649-665 https://doi.org/10.1007/s12273-020-0709-z
Research Article | Open Access | Issue | Published: 08 September 2020

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

Show Author's Information Hao-Cheng Zhu1,3,  Chen Ren1,  Shi-Jie Cao1,2( )
School of Architecture, Southeast University, 2 Sipailou, Nanjing, 21009, China
Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK
Academy of Building Energy Efficiency, School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
Keywords:
control, HVAC system, thermal comfort, indoor air pollution, indoor humidity
Cite this article:
Zhu H-C, Ren C, Cao S-J. Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system. Building Simulation, 2021, 14(3): 649-665. https://doi.org/10.1007/s12273-020-0709-z
Download citation
EndNote(RIS)
BibTeX

729

Views

83

Downloads

Citations

30

Crossref

N/A

WoS

33

Scopus

1

CSCD

Abstract Full text Electronic supplementary material About this article

Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO2, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI(T) and CRI(H), the distributions of indoor CO2 concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI(T) and LLHM-based CRI(H), respectively. Finally, according to the self-defined indices (i.e., EV, ET, EH), the optimal balancing between IEQ (indicated by CO2 concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.


menu
Abstract
Full text
Outline
Electronic supplementary material
About this article

Fast prediction for multi-parameters (concentration, temperature and humidity) of indoor environment towards the online control of HVAC system

Show Author's information Hao-Cheng Zhu1,3, Chen Ren1, Shi-Jie Cao1,2( )
School of Architecture, Southeast University, 2 Sipailou, Nanjing, 21009, China
Global Centre for Clean Air Research (GCARE), Department of Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, UK
Academy of Building Energy Efficiency, School of Civil Engineering, Guangzhou University, Guangzhou 510006, China

Abstract

Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO2, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI(T) and CRI(H), the distributions of indoor CO2 concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI(T) and LLHM-based CRI(H), respectively. Finally, according to the self-defined indices (i.e., EV, ET, EH), the optimal balancing between IEQ (indicated by CO2 concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.

Keywords: control, HVAC system, thermal comfort, indoor air pollution, indoor humidity

References(48)

Anand P, Cheong D, Sekhar C (2020). Computation of zone-level ventilation requirement based on actual occupancy, plug and lighting load information. Indoor and Built Environment, 29: 558-574.
DOI Google Scholar
ASHRAE (2009). ASHRAE Handbook: Fundamentals. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
Bluyssen PM, Aries M, van Dommelen P (2011). Comfort of workers in office buildings: The European HOPE project. Building and Environment, 46: 280-288.
DOI Google Scholar
Cao S-J, Zhu D-H, Yang Y-B (2016). Associated relationship between ventilation rates and indoor air quality. RSC Advances, 6: 111427-111435.
DOI Google Scholar
Cao S-J, Ren C (2018). Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network. Building and Environment, 144: 316-333.
DOI Google Scholar
Cao S-J (2019). Challenges of using CFD simulation for the design and online control of ventilation systems. Indoor and Built Environment, 28: 3-6.
DOI Google Scholar
Cao S-J, Deng H-Y (2019). Investigation of temperature regulation effects on indoor thermal comfort, air quality, and energy savings toward green residential buildings. Science and Technology for the Built Environment, 25: 309-321.
DOI Google Scholar
Cao S-J, Ding J, Ren C (2020). Sensor deployment strategy using cluster analysis of Fuzzy C-Means Algorithm: Towards online control of indoor environment's safety and health. Sustainable Cities and Society, 59: 102190.
DOI Google Scholar
Chen C, Zhao B, Lai D, Liu W (2018). A simple method for differentiating direct and indirect exposure to exhaled contaminants in mechanically ventilated rooms. Building Simulation, 11: 1039-1051.
DOI Google Scholar
Chow WK (2002). Ventilation of enclosed train compartments in Hong Kong. Applied Energy, 71: 161-170.
DOI Google Scholar
Deng Y, Feng Z, Fang J, Cao S-J (2018). Impact of ventilation rates on indoor thermal comfort and energy efficiency of ground-source heat pump system. Sustainable Cities and Society, 37: 154-163.
DOI Google Scholar
Egawa M, Oguri M, Kuwahara T, Takahashi M (2002). Effect of exposure of human skin to a dry environment. Skin Research and Technology, 8: 212-218.
DOI Google Scholar
Fang X, Winkler J, Christensen D (2011). Using EnergyPlus to perform dehumidification analysis on Building America homes. HVAC&R Research, 17: 268-283.
DOI Google Scholar
Feng Z, Zhou X, Xu S, Ding J, Cao S-J (2018). Impacts of humidification process on indoor thermal comfort and air quality using portable ultrasonic humidifier. Building and Environment, 133: 62-72.
DOI Google Scholar
Fisk WJ, Lei-Gomez Q, Mendell MJ (2007). Meta-analyses of the associations of respiratory health effects with dampness and mold in homes. Indoor Air, 17: 284-296.
DOI Google Scholar
Ganchev I, Taneva A, Kutryanski K, Petrov M (2019). Decoupling fuzzy-neural temperature and humidity control in HVAC systems. IFAC-PapersOnLine, 52: 299-304.
DOI Google Scholar
GB50019-2003 (2004). Code for Design of Heating Ventilation and Air Conditioning. Ministry of Construction of China. (in Chinese)
GB50736-2012 (2012). Design Code for Heating, Ventilation and Air Conditioning in Residential Buildings. Ministry of Housing and Urban-Rural Development of China. (in Chinese)
DOI
Grosskopf KR, Oppenheim P, Brennan T (2008). Preventing defect claims in hot, humid climates. ASHRAE Journal, 50(7): 40-41.
DOI Google Scholar
Gul MS, Patidar S (2015). Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy and Buildings, 87: 155-165.
Google Scholar
Hashiguchi N, Hirakawa M, Tochihara Y, Kaji Y, Karaki C (2005). Thermal environment and subjective responses of patients and staff in a hospital during winter. Journal of Physiological Anthropology and Applied Human Science, 24: 111-115.
DOI Google Scholar
He M, Li N, He Y, He D, Wang K (2017). Influences of temperature and humidity on perceived air quality with radiant panel workstation. Procedia Engineering, 205: 765-772.
DOI Google Scholar
Huang H, Kato S, Hu R, Ishida Y (2011). Development of new indices to assess the contribution of moisture sources to indoor humidity and application to optimization design: Proposal of CRI(H) and a transient simulation for the prediction of indoor humidity. Building and Environment, 46: 1817-1826.
DOI Google Scholar
Jaakkola K, Saukkoriipi A, Jokelainen J, Juvonen R, Kauppila J, et al. (2014). Decline in temperature and humidity increases the occurrence of influenza in cold climate. Environmental Health, 13: 22.
DOI Google Scholar
Jia J, Lee WL (2015). Experimental investigations on using phase change material for performance improvement of storage-enhanced heat recovery room air-conditioner. Energy, 93: 1394-1403.
DOI Google Scholar
Kim JT, Yu CWF (2018). Sustainable development and requirements for energy efficiency in buildings—The Korean perspectives. Indoor and Built Environment, 27: 734-751.
DOI Google Scholar
Lai D, Chen B, Liu K (2020). Quantification of the influence of thermal comfort and life patterns on outdoor space activities. Building Simulation, 13: 113-125.
DOI Google Scholar
Li W, Koo C, Cha SH, Hong T, Oh J (2018). A novel real-time method for HVAC system operation to improve indoor environmental quality in meeting rooms. Building and Environment, 144: 365-385.
DOI Google Scholar
Moroşan PD, Bourdais R, Dumur D, Buisson J (2010). Building temperature regulation using a distributed model predictive control. Energy and Buildings, 42: 1445-1452.
DOI Google Scholar
Paasi J, Nurmi S, Vuorinen R, Strengell S, Maijala P (2001). Performance of ESD protective materials at low relative humidity. Journal of Electrostatics, 51-52: 429-434.
DOI Google Scholar
de Paula Xavier AA, Lamberts R (2000). Indices of thermal comfort developed from field survey in Brazil. ASHRAE Transactions, 106(1): 45-58.
Google Scholar
Pérez-Lombard L, Ortiz J, Pout C (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394-398.
DOI Google Scholar
Popescu-Croitoru C (2011). Theoretical and experimental studies concerning the influence of air turbulence in conditioned rooms on thermal comfort. PhD Thesis, Technical University of Civil Engineering, Romania.
Razjouyan J, Lee H, Gilligan B, Lindberg C, Nguyen H, et al. (2020). Wellbuilt for wellbeing: Controlling relative humidity in the workplace matters for our health. Indoor Air, 30: 167-179.
DOI Google Scholar
Ren C, Cao S-J (2019a). Development and application of linear ventilation and temperature models for indoor environmental prediction and HVAC systems control. Sustainable Cities and Society, 51: 101673.
DOI Google Scholar
Ren J, Cao S-J (2019b). Incorporating online monitoring data into fast prediction models towards the development of artificial intelligent ventilation systems. Sustainable Cities and Society, 47: 101498.
DOI Google Scholar
Ren C, Cao S-J (2020). Implementation and visualization of artificial intelligent ventilation control system using fast prediction models and limited monitoring data. Sustainable Cities and Society, 52: 101860.
DOI Google Scholar
Sato M, Fukayo S, Yano E (2003). Adverse environmental health effects of ultra-low relative humidity indoor air. Journal of Occupational Health, 45: 133-136.
DOI Google Scholar
Shehadi M (2018). Review of humidity control technologies in buildings. Journal of Building Engineering, 19: 539-551.
DOI Google Scholar
Tian S, Su X, Shao X, Wang L (2020). Optimization and evaluation of a solar energy, heat pump and desiccant wheel hybrid system in a nearly zero energy building. Building Simulation,
DOI Google Scholar
Wang H, Wang G, Li X (2020). Implementation of demand-oriented ventilation with adjustable fan network. Indoor and Built Environment, 29: 621-635.
DOI Google Scholar
Yuan X, Pan Y, Yang J, Wang W, Huang Z (2020). Study on the application of reinforcement learning in the operation optimization of HVAC system. Building Simulation,
DOI Google Scholar
Zhang W, Hiyama K, Kato S, Ishida Y (2013). Building energy simulation considering spatial temperature distribution for nonuniform indoor environment. Building and Environment, 63: 89-96.
DOI Google Scholar
Zhang T, Liu X, Jiang Y (2014). Development of temperature and humidity independent control (THIC) air-conditioning systems in China—A review. Renewable and Sustainable Energy Reviews, 29: 793-803.
DOI Google Scholar
Zhang S, Cheng Y, Huan C, Lin Z (2019). Equivalent room air temperature based cooling load estimation method for stratum ventilation and displacement ventilation. Building and Environment, 148: 67-81.
DOI Google Scholar
Zhong Z, Xu X, Zhang X, Huang Z (2017). Simulation based control performance evaluation of a novel fuzzy logic control algorithm for simultaneously controlling indoor air temperature and humidity using a direct expansion (DX) air-conditioning (A/C) system. Procedia Engineering, 205: 1792-1799.
DOI Google Scholar
Zhu H, Yu CW, Cao S (2020). Ventilation online monitoring and control system from the perspectives of technology application. Indoor and Built Environment, 29: 587-602.
DOI Google Scholar
Zhuang C, Wang S (2020). Uncertainty-based robust optimal design of cleanroom air-conditioning systems considering life-cycle performance. Indoor and Built Environment,
DOI Google Scholar
File
12273_2020_709_MOESM1_ESM.pdf (1 MB)
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 08 May 2020
Accepted: 07 August 2020
Published: 08 September 2020
Issue date: June 2021

Copyright

© The Author(s) 2020

Acknowledgements

The authors would like to acknowledge the funding support from National Natural Science Foundation of China (No. 51778385).

Rights and permissions

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/

Return