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Modeling indoor air carbon dioxide concentration using artificial neural network

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Many studies have been conducted on estimating the number of occupants in a building to set the right ventilation rate in order to maintain standard indoor air quality. However, few have focused on predicting carbon dioxide itself based on the room’s available parameters, such as temperature and humidity. This study was aimed at predicting indoor air carbon dioxide concentration in a room using a multilayer perceptron neural network with relative humidity and temperature as inputs. The neural network is a popular data-driven method to provide geometry-independent prediction algorithms. In this study, the neural network was trained in three different ways with the complete, partial, and zero real carbon dioxide concentrations available in the learning process. The sensitivity and specificity analyses were conducted on the output. The most accurate model, based on the calculated mean-square-error method, was five-steps-ahead prediction model with less than 17 PPM difference on average to actual CO2 concentration in the room. Results were also promising for the open-loop model. Carbon dioxide predictions can be used in maintaining indoor air quality by improving ventilation control in buildings.

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  • Arens E, Zhang H, Hoyt T, Kaam S, Bauman F, Zhai Y, Paliaga G, Stein J, Seidl R, Tully B, Rimmer J, Toftum J (2015) Effects of diffuser airflow minima on occupant comfort, air mixing, and building energy use (RP-1515). Sci Technol Built Environ 21:1075–1090.

    Article  Google Scholar 

  • ASHRAE (2007) Standard 62-2007 (2007). Ventilation for acceptable indoor air quality. American Society of Heating, Refrigerating and Air-Conditioning Engineers Inc., Atlanta

    Google Scholar 

  • Calì D, Matthes P, Huchtemann K, Streblow R, Müller D (2015) CO2 based occupancy detection algorithm: experimental analysis and validation for office and residential buildings. Build Environ 86:39–49.

    Article  Google Scholar 

  • Candanedo LM, Feldheim V (2016) Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. Energy Build 112:28–39.

    Article  Google Scholar 

  • Collotta M, Messineo A, Nicolosi G, Pau G (2014) A dynamic fuzzy controller to meet thermal comfort by using neural network forecasted parameters as the input. Energies 7:4727–4756

    Article  Google Scholar 

  • Ebadat A, Bottegal G, Varagnolo D, Wahlberg B, Johansson KH (2013). Estimation of building occupancy levels through environmental signals deconvolution. In: Proceedings of the 5th ACM workshop on embedded systems for energy-efficient buildings. ACM, pp 1–8

  • Ebadat A, Bottegal G, Varagnolo D, Wahlberg B, Hjalmarsson H, Johansson KH (2015) Blind identification strategies for room occupancy estimation. In: Control conference (ECC), 2015 European. IEEE, pp 1315–1320

  • Fan Y, Kameishi K, Onishi S, Ito K (2014) Field-based study on the energy-saving effects of CO2 demand controlled ventilation in an office with application of energy recovery ventilators. Energy Build 68:412–422

    Article  Google Scholar 

  • Fisk WJ, De Almeida AT (1998) Sensor-based demand-controlled ventilation: a review. Energy Build 29:35–45.

    Article  Google Scholar 

  • Grace S, Mohan Lal D, Sharmeela C (2004) Demand controlled systems with fuzzy controllers to maintain indoor air quality—an energy saving approach. Int J Vent 3:79–86

    Article  Google Scholar 

  • Gruber M, Trüschel A, Dalenbäck J-O (2014) CO2 sensors for occupancy estimations: potential in building automation applications. Energy Build 84:548–556

    Article  Google Scholar 

  • Haykin SS (2009) Neural networks and learning machines. Pearson Education, Upper Saddle River

    Google Scholar 

  • Hoyt T, Arens E, Zhang H (2015) Extending air temperature setpoints: simulated energy savings and design considerations for new and retrofit buildings. Interact Hum Build Environ 88:89–96.

    Article  Google Scholar 

  • Jurelionis A, Isevičius E (2008) CFD predictions of indoor air movement induced by cold window surfaces. J Civ Eng Manag 14:29–38.

    Article  Google Scholar 

  • Kamendere E, Zogla G, Kamenders A, Ikaunieks J, Rochas C (2015) Analysis of mechanical ventilation system with heat recovery in renovated apartment buildings. Int Sci Conf Environ Clim Technol 72:27–33.

    Article  Google Scholar 

  • Laverge J, Van Den Bossche N, Heijmans N, Janssens A (2011) Energy saving potential and repercussions on indoor air quality of demand controlled residential ventilation strategies. Build Environ 46:1497–1503

    Article  Google Scholar 

  • Liddament M, Orme M (1998) Energy and ventilation. Appl Therm Eng 18:1101–1109

    Article  Google Scholar 

  • Lu T, Lü X, Viljanen M (2011) A novel and dynamic demand-controlled ventilation strategy for CO2 control and energy saving in buildings. Energy Build 43:2499–2508

    Article  Google Scholar 

  • Moschandreas DJ, Sofuoglu SC (2004) The indoor environmental index and its relationship with symptoms of office building occupants. J Air Waste Manag Assoc 54:1440–1451.

    Article  Google Scholar 

  • Panagopoulos IK, Karayannis AN, Kassomenos P, Aravossis K (2011) A CFD simulation study of VOC and formaldehyde indoor air pollution dispersion in an apartment as part of an indoor pollution management plan. Aerosol Air Qual Res 11:758–762

    Article  CAS  Google Scholar 

  • Pepper DW, Carrington D (2009) Modeling indoor air pollution. Imperial College Press, London

    Book  Google Scholar 

  • Ramponi R, Blocken B (2012) CFD simulation of cross-ventilation for a generic isolated building: Impact of computational parameters. Build Environ 53:34–48.

    Article  Google Scholar 

  • Schiavon S, Melikov AK, Sekhar C (2010) Energy analysis of the personalized ventilation system in hot and humid climates. Energy Build 42:699–707.

    Article  Google Scholar 

  • Seppänen O, Fisk W, Mendell M (1999) Association of ventilation rates and CO2 concentrations with health andother responses in commercial and institutional buildings. Indoor Air 9:226–252

    Article  Google Scholar 

  • Shan K, Sun Y, Wang S, Yan C (2012) Development and In-situ validation of a multi-zone demand-controlled ventilation strategy using a limited number of sensors. Build Environ 57:28–37.

    Article  Google Scholar 

  • Shendell DG, Prill R, Fisk WJ, Apte MG, Blake D, Faulkner D (2004) Associations between classroom CO2 concentrations and student attendance in Washington and Idaho. Indoor Air 14:333–341

    Article  CAS  Google Scholar 

  • Skön J, Johansson M, Raatikainen M, Leiviskä K, Kolehmainen M (2012) Modelling indoor air carbon dioxide (CO2) concentration using neural network. Presented at the International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, World Academy of Science, Engineering and Technology, pp 37–41

  • Sofuoglu SC (2008) Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings. Build Environ 43:1121–1126

    Article  Google Scholar 

  • Sun Z, Wang S, Ma Z (2011) In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone office building. Build Environ 46:124–133

    Article  Google Scholar 

  • Yang L, Ye M, He B-J (2014) CFD simulation research on residential indoor air quality. Sci Total Environ 472:1137–1144.

    Article  CAS  Google Scholar 

  • Zhong K, Kang Y (2009) Applicability of air-to-air heat recovery ventilators in China. Appl Therm Eng 29:830–840.

    Article  Google Scholar 

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Correspondence to B. Khazaei.

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Editorial responsibility: M. Abbaspour

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Khazaei, B., Shiehbeigi, A. & Haji Molla Ali Kani, A.R. Modeling indoor air carbon dioxide concentration using artificial neural network. Int. J. Environ. Sci. Technol. 16, 729–736 (2019).

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