Artificial Intelligence Techniques for Modeling Indoor Building Temperature under Tropical Climate Using Outdoor Environmental Monitoring
Publication: Journal of Energy Engineering
Volume 146, Issue 2
Abstract
In the current work, the artificial intelligence techniques multilayer perceptron neural network (MLP), radial basis function neural network (RBF), and group method of data handling (GMDH) were used to estimate the indoor temperature () in buildings under tropical climate conditions. The data used for modeling correspond to experimental samples measured during one year, considering as a case study the installation of a university laboratory. The models were developed utilizing as independent variables the climatological measurements of solar radiation, wind speed, outdoor relative humidity, and environmental temperature, and the working hours and occupancy of the building. The training, statistical comparison, and modeling performance were conducted through a computational methodology to obtain the best estimation architectures for each technique. The statistical parameter applied in the study were the root mean square error (RMSE) and the coefficient of correlation (R). Results reported the MLP as the technique with the best estimation accuracy ( and for training, and and for testing), with an architecture composed by 6-30-1 (input variables, hidden neurons, and output value). Additionally, a sensitivity analysis identified the MLP and RBF as the techniques that best represent the physical behavior of the phenomenon studied. According to the sensitivity analysis, the most influential variables were the environmental temperature and outdoor relative humidity, followed by solar irradiation, working hours, wind speed, and the number of occupants. The proposed methodology represents an alternative method to simplify the analysis of thermal modeling in buildings exposed to tropical climates based on an experimental measurement approach. It can be applied for the development of smart sensors aimed at the efficient management of energy and thermal comfort in buildings.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Acknowledgments
The authors thank the Program for the Professional Development of Teachers (PRODEP) No. 511-6/18-7798 and the Engineering Faculty of the Autonomous University of Yucatan (FIUADY) for the support granted during the development of this work. The authors also thank Eng. Adrian Carbajal and M.Eng. Renan Quijano for their support in the language revision phase.
References
Afroz, Z., T. Urmee, G. M. Shafiullah, and G. Higgins. 2018. “Real-time prediction model for indoor temperature in a commercial building.” Appl. Energy 231 (May): 29–53. https://doi.org/10.1016/j.apenergy.2018.09.052.
ASHRAE. 2013. Thermal environmental conditions for human occupancy. Standard 55-2013. Atlanta: ASHRAE.
Avini, R., P. Kumar, and S. J. Hughes. 2019. “Wind loading on high-rise buildings and the comfort effects on the occupants.” Sustainable Cities Soc. 45 (Feb): 378–394. https://doi.org/10.1016/j.scs.2018.10.026.
Azimi, H., H. Bonakdari, I. Ebtehaj, B. Gharabaghi, and F. Khoshbin. 2018. “Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length.” Acta Mech. 229 (3): 1197–1214. https://doi.org/10.1007/s00707-017-2043-9.
Beale, M. H., M. T. Hagan, and H. B. Demuth. 2017. Neural network toolbox user’s guide R2017a. Natick, MA: MathWorks.
Biswas, S. K., M. Chakraborty, H. R. Singh, D. Devi, B. Purkayastha, and A. K. Das. 2017. “Hybrid case-based reasoning system by cost-sensitive neural network for classification.” Soft Comput. 21 (Dec): 7579–7596. https://doi.org/10.1007/s00500-016-2312-x.
Blanco, J. M., A. Buruaga, E. Rojí, J. Cuadrado, and B. Pelaz. 2016. “Energy assessment and optimization of perforated metal sheet double skin façades through Design Builder; A case study in Spain.” Energy Build. 111 (Jan): 326–336. https://doi.org/10.1016/j.enbuild.2015.11.053.
Box, G. E. P. 1979. “Robustness in the strategy of scientific model building.” Robustness Stat. 201–236. https://doi.org/10.1016/B978-0-12-438150-6.50018-2.
Buonocore, C., R. De Vecchi, V. Scalco, and R. Lamberts. 2018. “Influence of relative air humidity and movement on human thermal perception in classrooms in a hot and humid climate.” Build. Environ. 146 (Dec): 98–106. https://doi.org/10.1016/j.buildenv.2018.09.036.
de Dear, R. 2004. “Thermal comfort in practice.” Supplement, Indoor Air 14 (S7): 32–39. https://doi.org/10.1111/j.1600-0668.2004.00270.x.
Djordjević, A. V., J. M. Radosavljević, A. V. Vukadinović, J. R. Malenović Nikolić, and I. S. Bogdanović Protić. 2017. “Estimation of indoor temperature for a passive solar building with a combined passive solar system.” J. Energy Eng. 143 (4): 04017008. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000437.
Evangelisti, L., C. Guattari, and F. Asdrubali. 2019. “On the sky temperature models and their influence on buildings energy performance: A critical review.” Energy Build. 183 (Jan): 607–625. https://doi.org/10.1016/j.enbuild.2018.11.037.
Faizollahzadeh Ardabili, S., A. Mahmoudi, and T. Mesri Gundoshmian. 2016. “Modeling and simulation controlling system of HVAC using fuzzy and predictive (radial basis function, RBF) controllers.” J. Build. Eng. 6 (Jun): 301–308. https://doi.org/10.1016/j.jobe.2016.04.010.
Golbraikh, A., and A. Tropsha. 2002. “Beware of q2!” J. Mol. Graphics Modell. 20 (4): 269–276. https://doi.org/10.1016/S1093-3263(01)00123-1.
Hernández-Pérez, I., J. Xamán, E. V. Macías-Melo, K. M. Aguilar-Castro, I. Zavala-Guillén, I. Hernández-López, and E. Simá. 2018. “Experimental thermal evaluation of building roofs with conventional and reflective coatings.” Energy Build. 158 (Jan): 569–579. https://doi.org/10.1016/j.enbuild.2017.09.085.
Ishola, N. B., A. A. Okeleye, A. S. Osunleke, and E. Betiku. 2019. “Process modeling and optimization of sorrel biodiesel synthesis using barium hydroxide as a base heterogeneous catalyst: Appraisal of response surface methodology, neural network and neuro-fuzzy system.” Neural Comput. Appl. 31 (9): 4929–4943. https://doi.org/10.1007/s00521-018-03989-7.
Ivakhnenko, A. G. 1970. “Heuristic self-organization in problems of engineering cybernetics.” Automatica 6 (2): 207–219. https://doi.org/10.1016/0005-1098(70)90092-0.
Jiménez-Xamán, C., J. Xamán, N. O. Moraga, I. Hernández-Pérez, I. Zavala-Guillén, J. Arce, and M. J. Jiménez. 2019. “Solar chimneys with a phase change material for buildings: An overview using CFD and global energy balance.” Energy Build. 186 (Mar): 384–404. https://doi.org/10.1016/j.enbuild.2019.01.014.
Kalami Heris, S. M. 2015. “Group method of data handling (GMDH) in MATLAB: Yarpiz.” Accessed January 28, 2019. http://yarpiz.com/263/ypml113-gmdh.
Laarabi, B., O. May Tzuc, D. Dahlioui, A. Bassam, M. Flota-Bañuelos, and A. Barhdadi. 2019. “Artificial neural network modeling and sensitivity analysis for soiling effects on photovoltaic panels in Morocco.” Superlattices Microstruct. 127 (Mar): 139–150. https://doi.org/10.1016/j.enbuild.2010.09.001.
Lan, L., P. Wargocki, and Z. Lian. 2011. “Quantitative measurement of productivity loss due to thermal discomfort.” Energy Build. 43 (5): 1057–1062. https://doi.org/10.1016/j.enbuild.2010.09.001.
Li, K., X. Xie, W. Xue, X. Dai, X. Chen, and X. Yang. 2018. “A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction.” Energy Build. 174 (Sep): 323–334. https://doi.org/10.1016/j.enbuild.2018.06.017.
Lu, T., and M. Viljanen. 2009. “Prediction of indoor temperature and relative humidity using neural network models: Model comparison.” Neural Comput. Appl. 18 (4): 345–357. https://doi.org/10.1007/s00521-008-0185-3.
Luo, M., Z. Wang, G. Brager, B. Cao, and Y. Zhu. 2018. “Indoor climate experience, migration, and thermal comfort expectation in buildings.” Build. Environ. 141 (Aug): 262–272. https://doi.org/10.1016/j.buildenv.2018.05.047.
Maca, P., P. Pech, and J. Pavlasek. 2014. “Comparing the selected transfer functions and local optimization methods for neural network flood runoff forecast.” Math. Prob. Eng. 2014 (Jul): 1–10. https://doi.org/10.1155/2014/782351.
Marino, C., A. Nucara, and M. Pietrafesa. 2017. “Thermal comfort in indoor environment: Effect of the solar radiation on the radiant temperature asymmetry.” Sol. Energy 144 (Mar): 295–309. https://doi.org/10.1016/j.solener.2017.01.014.
May Tzuc, O., A. Bassam, P. E. Mendez-Monroy, and I. S. Dominguez. 2018. “Estimation of the operating temperature of photovoltaic modules using artificial intelligence techniques and global sensitivity analysis: A comparative approach.” J. Renewable Sustainable Energy 10 (3): 033503. https://doi.org/10.1063/1.5017520.
Moody, J., and C. J. Darken. 1989. “Fast learning in networks of locally-tuned processing units.” Neural Comput. 1 (2): 281–294. https://doi.org/10.1162/neco.1989.1.2.281.
Mustafaraj, G., J. Chen, and G. Lowry. 2010. “Thermal behaviour prediction utilizing artificial neural networks for an open office.” Appl. Math. Modell. 34 (11): 3216–3230. https://doi.org/10.1016/j.apm.2010.02.014.
Nematchoua, M. K., P. Ricciardi, J. A. Orosa, S. Asadi, and R. Choudhary. 2019. “Influence of indoor environmental quality on the self-estimated performance of office workers in the tropical wet and hot climate of Cameroon.” J. Build. Eng. 21 (Jan): 141–148. https://doi.org/10.1016/j.jobe.2018.10.007.
Onwubolu, G. 2016. GMDH methodology and implementation in MATLAB, edited by S. M. V. Vishnu Mohan. London: Imperial College Press.
Özbalta, T. G., A. Sezer, and Y. Yildiz. 2012. “Models for prediction of daily mean indoor temperature and relative humidity: Education building in Izmir, Turkey.” Indoor Built Environ. 21 (6): 772–781. https://doi.org/10.1177/1420326X11422163.
Pandey, S., D. A. Hindoliya, and R. Mod. 2012. “Artificial neural networks for predicting indoor temperature using roof passive cooling techniques in buildings in different climatic conditions.” Appl. Soft Comput. J. 12 (3): 1214–1226. https://doi.org/10.1016/j.asoc.2011.10.011.
Pianosi, F., K. Beven, J. Freer, J. W. Hall, J. Rougier, D. B. Stephenson, and T. Wagener. 2016. “Sensitivity analysis of environmental models: A systematic review with practical workflow.” Environ. Modell. Software 79 (May): 214–232. https://doi.org/10.1016/j.envsoft.2016.02.008.
Platon, R., V. R. Dehkordi, and J. Martel. 2015. “Hourly prediction of a building’s electricity consumption using case-based reasoning, artificial neural networks and principal component analysis.” Energy Build. 92 (Apr): 10–18. https://doi.org/10.1016/j.enbuild.2015.01.047.
Qi, C., W. Wang, and S. Wang. 2015. “Application of indoor temperature prediction based on SVM and BPNN.” In Proc., 2015 27th Chinese Control and Decision Conf., CCDC 2015, 2883–2887. New York: IEEE.
Radosavljevic, J. M., M. R. Lambic, E. R. Mihajlovic, and A. V. Djordjevic. 2012. “Estimation of indoor temperature for a direct-gain passive solar building.” J. Energy Eng. 140 (1): 04013007. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000104.
Rahman, A., and A. D. Smith. 2018. “Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms.” Appl. Energy 228 (Oct): 108–121. https://doi.org/10.1016/j.apenergy.2018.06.064.
Rayegani, F., and G. C. Onwubolu. 2014. “Fused deposition modelling (FDM) process parameter prediction and optimization using group method for data handling (GMDH) and differential evolution (DE).” Int. J. Adv. Manuf. Technol. 73 (1–4): 509–519. https://doi.org/10.1007/s00170-014-5835-2.
Rosenblatt, F. 1958. “The perceptron: A probabilistic model for information storage and organization in the brain.” Psychol. Rev. 65 (6): 386–408. https://doi.org/10.1037/h0042519.
Roy, P. P., and K. Roy. 2008. “On some aspects of variable selection for partial least squares regression models.” QSAR Comb. Sci. 27 (3): 302–313. https://doi.org/10.1002/qsar.200710043.
Saber, E. M., K. W. Tham, and H. Leibundgut. 2016. “A review of high temperature cooling systems in tropical buildings.” Build. Environ. 96 (Feb): 237–249. https://doi.org/10.1016/j.buildenv.2015.11.029.
Seyedzadeh, S., F. P. Rahimian, I. Glesk, and M. Roper. 2018. “Machine learning for estimation of building energy consumption and performance: A review.” Visual Eng. 6 (1): 5. https://doi.org/10.1186/s40327-018-0064-7.
Simons, H. 2009. Neural networks and learning machines. Hamilton, ON, Canada: Pearson Education, McMaster Univ.
Taghinia, J., M. M. Rahman, and T. Siikonen. 2015. “Numerical simulation of airflow and temperature fields around an occupant in indoor environment.” Energy Build. 104 (Oct): 199–207. https://doi.org/10.1016/j.enbuild.2015.06.085.
Thomas, B., and M. Soleimani-Mohseni. 2007. “Artificial neural network models for indoor temperature prediction: Investigations in two buildings.” Neural Comput. Appl. 16 (1): 81–89. https://doi.org/10.1007/s00521-006-0047-9.
Wang, Z., R. S. Srinivasan, and J. Shi. 2016. “Artificial intelligent models for improved prediction of residential space heating.” J. Energy Eng. 142 (4): 04016006. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000342.
Zendehboudi, A., and A. Tatar. 2017. “Utilization of the RBF network to model the nucleate pool boiling heat transfer properties of refrigerant-oil mixtures with nanoparticles.” J. Mol. Liq. 247 (Dec): 304–312. https://doi.org/10.1016/j.molliq.2017.09.105.
Zingre, K. T., E. H. Yang, and M. P. Wan. 2017. “Dynamic thermal performance of inclined double-skin roof: Modeling and experimental investigation.” Energy 133 (Aug): 900–912. https://doi.org/10.1016/j.energy.2017.05.181.
Information & Authors
Information
Published In
Journal of Energy Engineering
Volume 146 • Issue 2 • April 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Mar 30, 2019
Accepted: Sep 24, 2019
Published online: Feb 13, 2020
Published in print: Apr 1, 2020
Discussion open until: Jul 13, 2020
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.