A NOVEL OCCUPANT DETECTION-BASED VENTILATION CONTROL STRATEGY FOR SMART BUILDING APPLICATIONS

Yıl 2021, Cilt: 7 Sayı: 2, 24 - 35, 31.12.2021

Cihan Turhan , Aydın Ege Çeter

https://doi.org/10.22531/muglajsci.928315

Cited By: 1

Öz

Traditional ventilation systems supply fresh air to the building on a consistent basis, however, do not ensure adequate comfortable areas in indoor environments. On the other hand, smart ventilation systems adjust indoor environment parameters optionally by occupancy schedules to provide desired indoor air quality while minimizing energy consumption. ASHRAE Standard 62, which shows ventilation strategies for acceptable indoor air quality, uses steady-state occupancy detection algorithms, however, dynamic methods are required behind simulation studies. To this aim, this study presents a dynamic novel ventilation control strategy by detecting occupants in an office building in Ankara-Turkey. The novel control strategy includes deployment of several sensors which measure carbon dioxide concentration, indoor air temperature and position of the door. A prototype of the controller is manufactured and tested in a real environment with the help of regulation of air flow in HVAC system. The proposed control strategy is tested between January 1st, 2020 and August 15th, 2020 and compared with traditional controller in terms of thermal comfort and energy consumption. The results showed that proposed controller decreased energy consumption by 16% while satisfying thermal comfort for the 94% of total number of occupants.

Anahtar Kelimeler

Occupant detection, Smart buildings, Ventilation systems, Indoor air quality, Door position

Kaynakça

• N. Payam, J. Fatemeh, M. T. Mohammad, G. Mohammad, and M. A. Muhd Zaimi, “A global review ofenergy consumption, CO2 emissions and policy in the residential sector (with an overview of the top tenCO2emitting countries),” Renewable and Sustainable Energy Reviews, 43, 843–862, 2015.
• Singh, S. and Kennedy, C., “Estimating future energy use and CO2 emissions of the world's cities”, Environmental Pollution, 203, 271-278, 2015.
• Escriva-Escriva, G., Segura-Heras, I. and Alcazar-Ortega, M., “Application of an energy management and control system to assess the potential of different control strategies in HVAC systems”, Energy and Buildings, 42(11), 2258-2267, 2010.
• Rashid, S. A., Haider, Z., Hossain, S. M. C., Memon, K., Panhwar, F., Mbogba, M. K., Hu, P. and Zhao, G., “Retrofitting low-cost heating ventilation and air-conditioning systems for energy management in buildings”, Applied Energy, 236, 648-661, 2019.
• American Society of Heating, Refrigerating and Air-Conditioning Engineers, 2019. Ventilation for Acceptable Indoor Air Quality (Standard 62.1). Atlanta, GA: ASHRAE.
• Turhan, C., “Development of Energy Efficient Personalized Thermal Comfort Driven Control in HVAC Systems”, (PhD Thesis), Izmir Institute of Technology, İzmir, 127, 2018.
• Yang, Z. and Becerik-Gerber, B., “The coupled effects of personalized occupancy profile-based HVAC schedules and room reassignment on building energy use”, Energy and Buildings, 78, 113-122, 2014.
• Ansanay-Alex, G., “Estimating Occupancy Using Indoor Carbon Dioxide Concentrations Only in an Office Building: a Method and Qualitative Assessment”, In: 11th REHVA World Congress ''Energy efficient, smart and healthy buildings”: Clima, Prague, June 2013.
• Wang, S. W. and Jin, X. Q., “CO2-based occupancy detection for on-line outdoor air flow control”, Indoor and Built Environment, 7(3), 165-181, 1998.
• Cali, D., Matthes, P., Huchtmann, K., Streblow, R. and Muller, D., “CO2 based occupancy detection algorithm: Experimental analysis and validation for office and residential buildings”, Building and Environment, 86, 39-49, 2015.
• Hobson, B. W., Lowcay, D., Gunay, H. B., Ashouri, A. and Newsham, G. R., “Opportunistic occupancy-count estimation using sensor fusion: A case study”, Building and Environment, 159, 106154, 2019.
• Candenedo, L. M. and Feldheim, V., “Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models”, Energy and Buildings, 112, 28-39, 2016.
• Masood, M. K., Soh, Y. C. and Chang, V. W. C., 2015. “Real-time occupancy estimation using environmental parameters”, In: International Joint Conference on Neural Networks, Killarney, Ireland on 12-17 July 2015.
• Amayri, M., Arora, A., Ploix, S., Bandhyopadyay, S., Ngo, Q. D. and Badarla, V. R., “Estimating occupancy in heterogeneous sensor environment”, Energy and Buildings, 129, 46-58, 2016.
• Yang, Z., Li, N., Becerik-Gerber, B. and Orosz, M., “A systematic approach to occupancy modelling in ambient sensor-rich buildings”,” Simulation-Transactions of the Society for Modelling and Simulation International, 90(8), 960-977, 2014.
• Yang, Z., Li, N., Becerik-Gerber, B. and Orosz, M., “A Non-Intrusive Occupancy Monitoring System for Demand Driven HVAC Operations” In: Construction Research Congress, West Lafayette, Indiana, United States on 21-23 May 2012, 828-837.
• Abade, B., Perez Abreu, D. and Curado, M., “A Non-Intrusive Approach for Indoor Occupancy Detection in Smart Environments”, Sensors (Basel), 18(11):3953, 2018.
• Han, H., Jang K., Han, C. and Lee, J., “Occupancy Estimation Based On CO2 Concentration Using Dynamic Neural Network Model”, In: AIVC34, Athens, Greece on 25-26 September 2013, 443-450.
• Kim, S., Song, Y., Sung, Y. and Seo, D., “Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modelling Tools”, Energies, 12(3), 433, 2019.
• Rahman, H. and Han, H., “Bayesian estimation of occupancy distribution in a multi-room office building based on CO2 concentrations”, Building Simulation, 11, 575–583, 2018.
• http://koeppen-geiger.vu-wien.ac.at/present.htm (05.04.2020)
• Yılmaz, E. and Çiçek, İ. “Detailed Köppen-Geiger climate regions of Turkey - Türkiye’nin detaylandırılmış Köppen-Geiger iklim bölgeleri”, Journal of Human Sciences, 15(1), 225-242, 2018.
• https://www.mgm.gov.tr/veridegerlendirme/gun-derece.aspx?g=yillik&m=06-00&y=2019&a=05#sfB (05.04.2020)
• Turkish Standardization Instution, 2008. TS 825 Thermal Insulation Requirements in Buildings. Ankara, Turkey: TSE.
• American Society of Heating, Refrigerating and Air-Conditioning Engineers, 2017. Thermal Environment Conditions for Human Occupancy (Standard 55). Atlanta, GA: ASHRAE.
• DF Robots, MG811, Carbon dioxide Sensor Datasheet, Retrieved from: https://wiki.dfrobot.com/CO2_Sensor_SKU_SEN0159#target_0 15.10.2020.
• DF Robots, DHT22, Temperature & Relative Humidity Sensor Datasheet, Retrieved from: https://wiki.dfrobot.com/DHT22_Temperature_and_humidity_module_SKU_SEN0137 15.10.2020.
• SparkFun Electronics, HC – SR04, Ultrasonic Ranging Module Datasheet, Retrieved from: https://www.digikey.com/htmldatasheets/production/1979760/0/0/1/hc-sr04.html 15.10.2020.
• Turhan, C., Kazanazmaz, T., Uygun, I. E., Ekmen, K. E. and Akkurt, G. G., “Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation”, Energy and Buildings, 85, 115-125, 2014.
• Turhan, C., Simani, S., Zajic, I. and Akkurt, G. G., “Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model”, Energies, 10(1), 67, 2017.
• Mathworks, MATLAB, 2019.
• European Committee for Standardization, 2007. EN 15521-Indoor Environmental Input Parameters for Design and Assessment of Energy Performance of Buildings – Addressing Indoor Air Quality, Thermal Environment, Lighting and Acoustics. Brussels, Belgium: CEN.
• European Committee for Standardization, 2009. EN16798-Energy performance of buildings - Ventilation for buildings - Part 1. Brussels, Belgium: CEN.
• Charles, K. E. and Veitch, A. J., “Environmental Satisfaction in Open-Plan Environments: 2. Effects of Workstation Size, Partition Height and Windows”. Ontario, Canada: IRC, 2002.
• Turhan, C., “Comparison of Indoor Air Temperature and Operative Temperature-Driven HVAC systems by Means of Thermal Comfort and Energy Consumption”, Muğla Journal of Science and Technology, 6, 156-173, 2020.