Thermal behaviour prediction utilizing artificial neural networks for an open office

Applied Mathematical Modelling

Volumn 34, Issue 11, 2010, Pages 3216-3230

  Mustafaraj, G ^a   Chen, J ^a   Lowry, G ^b  

^a BRUNEL UNIVERSITY   (United Kingdom)

^b LONDON SOUTH BANK UNIVERSITY   (United Kingdom)

Author keywords

Black box non linear neural network models; Building management system; Optimal brain surgeon pruning algorithm; Room temperature and relative humidity prediction

Indexed keywords

BLACK BOXES; BUILDING MANAGEMENT SYSTEM; NONLINEAR NEURAL NETWORKS; OPTIMAL BRAIN SURGEON; PRUNING ALGORITHMS; ROOM TEMPERATURE;

ALGORITHMS; ATMOSPHERIC HUMIDITY; BRAIN MODELS; FORECASTING; INTELLIGENT BUILDINGS; MANAGEMENT; MOISTURE; NEURAL NETWORKS; OFFICE BUILDINGS; OPTIMIZATION;

CLIMATE MODELS;

EID: 77952953206     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2010.02.014     Document Type: Article

References (28)

1. Seginer I., Boulard T., Bailey J. Neural networks models of the greenhouse climate. J. Agric. Eng. Res. 1994, 59:203-216.
2. Kreider J.F., Claridge D.E., Curtiss P., Dodier R., Haberl J.S., Krarti M. Building energy use prediction and system identification using recurrent neural networks. J. Sol. Energy Eng. 1995, 117:161-166.
3. Ruano A.E., Crispim E.M., Conceicao E.Z.E., Lucio M.M.J.R. Predictions of building's temperature using neural networks models. Energy Buildings 2006, 38:682-694.
4. Ohlsson M., Peterson C., Pi H., Rognvaldsson T., Soderberg B. Predicting system loads with artificial neural networks - methods and results from " The great energy predictor shootout" ASHRAE Trans. 1994, 100(2):1063-1074.
5. Kreider J.F., Haberl J.S. Predicting hourly building energy use: the great energy Prediction Shootout - overview and discussion of results. ASHRAE Trans. 1994, 100:1104-1118.
6. Haberl J.S., Thamilseran S. The great energy Predictor Shootout II: measuring retrofit savings-overview and discussion of results. ASHRAE Trans. 1998, 102:419-435.
7. Karatasou S., Santamouris M., Geros V. Modelling and predicting building's energy use with artificial neural networks: methods and results. Energy Buildings 2006, 38:949-958.
8. Kawashima M. Artificial neural networks backpropagation model with three phase annealing developed for the building energy predictor shootout. ASHRAE Trans. 1994, 100(2):1096-1103.
9. M.R.B. Breekweg, P. Gruber, Modelling the energy consumption of buildings by means of neural networks, in: Proceeding of CESA 96: symposium on control, optimisation and supervision, vol. 1, 1996, pp. 450-455.
10. Breekweg M.R.B., Gruber P., Ahmed O. Development of a generalized neural network model to detect faults in building energy performance-part I, part II. ASHRAE Trans. 2000, 106:61-93.
11. Gonzales P.A., Zamarreno J.M. Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Buildings 2005, 37:595-601.
12. Frausto H.U., Pieters J.G. Modelling greenhouse temperature using system identification by means of neural networks. Neurocomputing 2004, 56:423-428.
13. Lu T., Viljanen M. Prediction of indoor temperature and relative humidity using neural network models: model comparison. Neural Comput. Appl. 2009, 18:345-357.
14. Thomas B., Mohseni M.S. Artificial neural network models for indoor temperature prediction: investigations in two buildings. Neural Comput. Appl. 2007, 16:81-89.
15. Patil S.L., Tantau H.J., Salokhe V.M. Modelling of tropical greenhouse temperature by autoregressive and neural network models. Biosyst. Eng. 2008, 99:423-431.
16. Mechaqrane A., Zouak M. A comparison of linear and neural network ARX models applied to a prediction of the indoor temperature of a building. Neural Comput. Appl. 2004, 13:32-37.
17. Gouda M.M., Danaher S., Underwood C.P. Application of an artificial neural network for modelling the thermal dynamics of a building's space and its heating system. Math. Computer Model. Dyn. Syst. 2002, 8:333-344.
18. Mohseni M.S., Thomas B., Fahlen P. Estimation of operative temperature in buildings using artificial neural networks. Energy Buildings 2006, 38:635-640.
19. Norgaard M., Ravn O., Poulsen N.K. NNSYSID-toolbox for system identification with neural networks. Math. Computer Model. Dyn. Syst. 2002, 8(1):1-20.
20. Chau C.Y.H., Hu J.S. Development of an enthalpy and carbon dioxide based demand control ventilation for indoor air quality and energy savings with neural networks control. Indoor Built Environ. 2004, 13:463-475.
21. Mandic D.P., Chambers J.A. Recurrent Neural Networks for Prediction 2001, John Wiley and Sons Ltd.
22. Norgaard M., Ravn O., Poulsen N.K., Hansen L.K. Neural Networks for Modelling and Control of Dynamic Systems 2000, Springer-Verlag.
23. Siegelmann H.T., Horne B.G., Giles C.L. Computational capabilities of recurrent NARX neural networks. IEEE Trans. Syst., Man, Cybern.-Part B: Cybern. 1997, 27(2):208-215.
24. Menezes J.M.P., Barreto G.A. Long-term time series prediction with the NARX network: an empirical evaluation. Neurocomputing 2008, 71:3335-3343.
25. Su H.T., McAvoy T.J., Werbo P. Long-term prediction of chemical processes using recurrent neural networks: a parallel training approach. Ind. Eng. Chem. Res. 1992, 31:1338-1352.
26. G. Mustafaraj, Thermal Behaviour Model Identification for Three Different Office Buildings, Ph.D. Thesis, Brunel University, United Kingdom, 2008.
27. Ljung L. System Identification: Theory for User 1999, Prentice-Hall, Upper Saddle River.
28. Sjoberg J., Zhang Q., Ljung L., Benveniste A., Delyon B., Glorennec P.Y., Hjalmarsson H., Juditsky A. Nonlinear black-box modelling in system identification: a unified overview. Automatica 1995, 31(12):1691-1724.