Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region

Energy and Buildings

Volumn 121, Issue , 2016, Pages 32-42

  Mba, Leopold ^a   Meukam, Pierre ^b   Kemajou, Alexis ^a  

^a UNIVERSITY OF DOUALA   (Cameroon)

^b UNIVERSITY OF YAOUNDÉ I   (Cameroon)

Author keywords

Artificial neural network; Building; Humid region; Indoor air temperature; Matlab; Relative humidity

Indexed keywords

AIR CONDITIONING; ATMOSPHERIC HUMIDITY; ATMOSPHERIC TEMPERATURE; BUILDINGS; ENERGY UTILIZATION; FORECASTING; HYPERBOLIC FUNCTIONS; MATLAB; STRUCTURAL OPTIMIZATION; THERMAL COMFORT;

COEFFICIENT OF CORRELATION; EXPERIMENTAL BUILDINGS; HUMID REGIONS; INDOOR AIR TEMPERATURE; INDOOR AND OUTDOOR AIRS; LEVENBERG-MARQUARDT ALGORITHM; MULTI LAYER PERCEPTRON; REDUCE ENERGY CONSUMPTION;

NEURAL NETWORKS;

EID: 84964054524     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2016.03.046     Document Type: Article

References (48)

1. R. Holz, A. Hourigan, R. Sloop, P. Monkman, and M. Krarti Effects of standard energy conserving measures on thermal comfort Build. Environ. 32 1 1997 31 43
2. K.W. Tham, and M.B. Ullah Building energy performance and thermal comfort in Singapore ASHRAE Trans. 99 1 1993 308 321
3. Turkan Goksal Ozbalta, Alper Sezer, and Yusuf Yildiz Models for prediction of daily mean indoor temperature and relative humidity: education building in Izmir, Turkey Indoor Built Environ. 21 6 2012 772 781
4. T. Catalina, J. Virgone, and E. Blanco Development and validation of regression models to predict monthly heating demand for residential buildings Energy Build. 40 2008 1825 1832
5. N. Mendes, G.H.C. Oliveira, H.X. Araujo, and L.S. Coelho A Matlab-based simulation tool for building thermal performance analysis Eighth International IBPSA Conference Eindhoven, Netherlands 2003 8 p
6. Papamicheal Konstantinos, and Pal Vineeta Barriers in developing and using simulation-based decision-support software Presented at the ACEEE 2002 Summer Study on Energy Efficiency in Building August 18-23, and published in the proceedings 2002 5 p
7. R.H. Dodier, and G.P. Henze Statistical analysis of neural networks as applied to building energy prediction Trans. ASME 126 1 2004 592 600
8. Rakoczy Masiuk Application of artificial neural network to assessment of influence of operational parameters on the characteristic quantities of thermal transient process in a reciprocating mixer 5th European Thermal-Sciences Conference the Netherlands 2008 8 p
9. Tawfiq AI-Saba, and Ibrahim EI-Amin Artificial neural networks as applied to long-term demand forecasting Artif. Intell. Eng. 13 1999 189 197
10. H. Huang-Chu, H. Rey-Chue, and Jer-guang Hsieh A new artificial peak power load forecaster based on non fixed neural networks Electr. Power Energy Syst. 24 2001 245 250
11. Mba Léopold, Modélisation du comportement thermique du bâtiment: application d'une méthode neuronale. Université de Douala-Cameroun, mémoire de DEA présenté au laboratoire d'énergie de l'école doctorale des Sciences Fondamentales et Appliquées, 2009, 68 p.
12. G. Cybenko Approximation by superposition of a sigmoidal function Math. Control Signal Syst. 2 1989 303 314
13. Bauer Manuel, Gestion biomimétique de l'énergie dans le bâtiment. Ecole Polytechnique Fédérale de Lausanne. Thèse de doctorat présentée d'architecture, 1998, 189 p.
14. Imran Tasadduq, Shafiqur Rehman, and Khaled Bubshait Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia Renew. Energy 25 2002 545 554
15. Mba Léopold, A. Kemajou, P. Meukam, Application of artificial neural network for modeling the thermal behavior of building in humid region. In Formation, recherche, innovation et développement au cœur de l'interdisciplinarité. Actes des 3ème rencontres EG@ Yaoundé, Cameroun, 14-16 septembre 2010. L'Harmattan, 2012, pp. 168-184.
16. Cinzia Buratti, Elisa Lascaro, Domenico Palladino, and Marco Vergoni Building behavior simulation by means of artificial neural network in summer conditions Sustainability 6 2014 5339 5353
17. Valerio Lo Brano, Giuseppina Ciulla, and Mariavittoria Di Falco Artificial neural networks to predict the power output of PV panel Int. J. Photoenergy 2014 2014 1 12 Article ID 193083
18. J. Thibault, and B.P.A. Grandjean A neural network methodology for heat transfer data analysis Int. J. Heat Mass Transfer 34 1991 2063 2070
19. G.J. Zdaniuk Heat Transfer and Friction in Helically-finned Tubes Using Artificial Neural Networks. Ph.D. dissertation 2006 Mississippi State University
20. S. Yilmaz, and K. Atik Modeling of a mechanical cooling system with variable cooling capacity by using artificial neural network Appl. Therm. Eng. 27 2007 2308 2313
21. T.N. Singh, S. Sinha, and V.K. Singh Prediction of thermal conductivity of rock through physic-mechanical properties Build. Environ. 42 2007 146 155
22. J.F. Kreider Neural networks applied to building energy studies H. Bloem (Ed.), Workshop on Parameter Identification 1995 JRC Ispra Ispra 243 251
23. Subodh Kumar Sinha, Manju Rani, Gayatri, and P.K. Mandal Expression pattern analysis of different micro RNAs under nitrogen deprivation condition in root tissues of different wheat genotypes Indo Global J. Pharm. Sci. 4 3 2014 194
24. M.C. Alexiadis, P.S. Dokopoulos, H.S. Sahsamanoglou, and I.M. Manousarids Short-term forecasting of wind speed and related electrical power Sol. Energy 63 1 1998 61 69
25. E.C. Njau Prediction of meteorological parameters: I. Analytical method Nuovo Cimento 14C 1991 473 488
26. E.C. Njau Prediction of meteorological parameters: II. Methods based on an electronic system Proceedings of the International Conference on Global Warming and Climate Change Geneva, Switzerland 1993
27. G.V. Parishwad, R.K. Bhardwaj, and V.K. Nema Prediction of monthly-mean hourly relativehumidity, ambient temperature, and wind velocity for India Renew. Energy 13 3 1998 363 380
28. M. Soleimani-Moheseni, B. Thomas, and Fahien Per Estimation of operative temperature in buildings using artificial neural networks Energy Build. 38 2006 635 640
29. Tao Lu, and Martti Viljanen Prediction of indoor temperature and relative humidity using neural network models: model comparison Neural. Comput. Appl. 18 2009 345 357
30. G. Mustafaraj, J. Ghena, and G. Lowry Thermal behaviour prediction utilizing artificial neural networks for an open space Appl. Math. Model. 34 2010 3216 3230
31. Alexis Kemajou, Léopold Mba, and Pierre Meukam Application of artificial neural network for predicting the indoor air temperature in modern building in humid region Br. J. Appl. Sci. Technol. 2 1 2012 23 34
32. A. Mélingui, M. Gwanfogbe, J. Nguoghia, and J. Mounkam Nouvelle Géographie du cameroun 3ème EDICEF 1987 120 Pages
33. A. Mélingui, M. Kuete, J. Nguoghia, J. Mounkam, and D. Nofiole Nouvelle Géographie 3ème Nouvelle édition 1993 EDICEF
34. Météo, Météorologie Nationale du Cameroun, Tableaux Climatologiques Mensuels TCM. Direction Nationale de la météorologie. Douala, Cameroun, 2014.
35. Daniel Coakley, Paul Raftery, and Marcus Keane A review of methods to match building energy simulation models to measured data Renew. Sust. Energy Rev. 37 2014 123 141
36. ISO 7726 Ergonomics of the Thermal Environment - Instruments of Measuring Physical Quantities 1998 International Organization for Standardization Geneva
37. Modeste Kameni Nematchoua, Rene Tchinda, Wagner Augusto Andreasi, and Noel Djongyang Adaptive study of thermal comfort in equatorial zone: case of some buildings in Cameroon Int. J. Emerging Technol. Adv. Eng. 4 2 2014 36 47 www.ijetae.com (ISSN 2250-2459, ISO 9001:2008, Certified Journal)
38. Erick Johansson, Sofia Thorsson, Rohinton Emmanuel, and Eduardo Kruger Instruments and methods in outdoor thermal comfort studies - the need for standardization Urban Clim. 10 2014 346 366
39. T. Manssouri, H. Sahbi, I. Manssouri, and B. Boudad Utilisation d'un modèle hybride base sur la rlms et les rna-pmc pour la prédiction des paramètres indicateurs de la qualité des eaux souterraines cas de la nappe de Souss-Massa-Maroc Eur. Sci. J. 11 June 2015 35 46
40. Subodh Paudel, Mohamed Elmtiri, Wil L. Kling, Olivier Le Corre, and Bruno Lacarrière Pseudo dynamic transitional modeling of building heating energy demand using artificial neural network Energy Build. 2014 10.1016/j.enbuil.2013.11.051
41. S.A.M. Said Degree-day base temperature for residential building energy prediction in Saudia Arabia ASHRAE Trans. Res. 98 1 1992 346 353
42. D. Dreyfus, J.M. Martinez, M. Samuelides, M.B. Gordon, F. Badran, S. Thiria, and L. Hérault Réseaux de neurones, méthodologie et applications 2ème édition 2002 Eyrolles
43. Howard Demuth, Mark Beale, Martin Hagan, Neural Network Toolbox 5: User's Guide. Guide d'utilisation des réseaux de neurones sur le logiciel Matlab, 2007.
44. Chaabani, Modélisation du procédé d'hydrotraitement du gasoil à l'aide des réseaux de neurones artificiels. Université m'hamed bougara boumerdes. Mémoire de fin d'études présenté en Faculté des Hydrocarbures et de la Chimie. Algérie, 2008, 128 p.
45. S. Jassar, Z. Liao, and L. Zhao Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems Build. Environ. 44 2009 1609 1616
46. J.P. Corriou, Les réseaux de neurones pour la modélisation et la conduite des procédés. Lavoisier Technique et Documentation. Paris-France, 1995.
47. S. Haykin Neural Networks: A Comprehensive Foundation 1994 Prentice Hall Upper Saddle River, NJ
48. G. Panchal, A. Ganatra, Y.P. Kosta, and D. Panchal Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers Int. J. Comput. Theory Eng. 3 2 2011 332 337