Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks

Energy Conversion and Management

Volumn 50, Issue 1, 2009, Pages 90-96

  Li, Qiong ^a,b   Meng, Qinglin ^a   Cai, Jiejin ^c   Yoshino, Hiroshi ^b   Mochida, Akashi ^b  

^a SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

^b TOHOKU UNIVERSITY   (Japan)

^c UNIVERSITY OF TOKYO   (Japan)

Author keywords

Cooling load; Energy conservation; Neural networks; Prediction; Support vector machine

Indexed keywords

AIR CONDITIONING; ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; BUILDINGS; COOLING; ENERGY CONSERVATION; ENERGY MANAGEMENT; FEEDFORWARD NEURAL NETWORKS; FORECASTING; IMAGE CLASSIFICATION; IMAGE RETRIEVAL; LEARNING SYSTEMS; OFFICE BUILDINGS; RADIAL BASIS FUNCTION NETWORKS; STRUCTURAL DESIGN; SUPPORT VECTOR MACHINES; VECTORS; VEGETATION;

ARTIFICIAL NEURAL NETWORKS; BACK PROPAGATION NEURAL NETWORKS; BUILDING COOLING LOADS; COOLING LOAD; COOLING LOADS; GENERAL REGRESSION NEURAL NETWORKS; GUANGZHOU , CHINAS; MEAN RELATIVE ERRORS; MODELING TECHNIQUES; PREDICTION; PREDICTION ACCURACIES; PREDICTION MODELS; RADIAL BASIS FUNCTION NEURAL NETWORKS; ROOT MEAN SQUARE ERRORS; SIMULATION RESULTS; SUPPORT VECTOR MACHINE; SUPPORT VECTORS;

NEURAL NETWORKS;

EID: 56049088473     PISSN: 01968904     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enconman.2008.08.033     Document Type: Article

References (28)

1. Bida M., and Kreider J.F. Monthly-averaged cooling load calculations-residential and small commercial buildings. J Solar Energy Eng Trans ASME 109 4 (1987) 311-10
2. Vildan Ok. A procedure for calculating cooling load due to solar radiation: the shading effects from adjacent or nearby buildings. Energy Build 19 1 (1992) 11-10
3. Yao Y., Lian Z.W., Liu S.Q., and Hou Z.J. Hourly cooling load prediction by a combined forecasting model based on analytic hierarchy process. Int J Thermal Sci 43 11 (2004) 1107-1112
4. Lokmanhekim M., and Henninger R.H. Computerized energy requirements analysis and heating/cooling load calculations of buildings. ASHRAE J 14 4 (1972) 25-29
5. Milorad B., and Francis Y. Cooling energy evaluation for high-rise residential buildings in Hong Kong. Energy Build 37 4 (2005) 345-347
6. Ansari F.A., Mokhtar A.S., Abbas K.A., and Adam N.M. A simple approach for building cooling load estimation. Am J Environ Sci 1 3 (2005) 209-212
7. Chou S.K., and Chang W.L. Large building cooling load and energy use estimation. Int J Energy Res 21 2 (1997) 169-15
8. Sodha M.S., Kaur B., Kumar A., and Bansal N.K. A comparison of the admittance and fourier methods for predicting heating/cooling loads. Solar Energy 36 2 (1986) 125-3
9. Greg J.S., and Robert A.K. A correlation method for predicting monthly and annual cooling loads in direct gain passive solar heated buildings. Energy Convers Manage 29 3 (1989) 175-13
10. Bauer M., and Scartezzini J.L. A simplified correlation method accounting for heating and cooling loads in energy-efficient buildings. Energy Build 27 2 (1998) 147-148
11. Al-Rabghi O.M.A., and Al-Johani K.M. Utilizing transfer function method for hourly cooling load calculations. Energy Convers Manage 38 4 (1997) 319-14
12. Ben-Nakhi A.E., and Mahmoud M.A. Cooling load prediction for buildings using general regression neural networks. Energy Convers Manage 45 13-14 (2004) 2127-15
13. Mui K.W., and Wong L.T. Cooling load calculations in subtropical climate. Build Environ 42 7 (2007) 2498-7
14. Kalogirou S.A. Artificial neural networks in renewable energy systems applications: a review. Renew Sustainable Energy Rev 5 4 (2001) 373-29
15. Jin Y., Hugues R., and Radu Z. On-line building energy prediction using adaptive artificial neural networks. Energy Build 37 12 (2005) 1250-10
16. González P.A., and Zamarreño J.M. Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Build 37 6 (2005) 595-597
17. Yasar I., Akif K., and Cem P. Performance prediction for non-adiabatic capillary tube suction line heat exchanger: an artificial neural network approach. Energy Convers Manage 46 2 (2005) 223-10
18. Zhang X.X., and Sun C.X. Dynamic intelligent cleaning model of dirty electric load data. Energy Convers Manage 49 4 (2008) 564-566
19. Specht D.F. A general regression neural network. IEEE Trans Neural Networks 2 6 (1991) 568-569
20. Panom P., Phaophak S., Noppadol K., Khamron S., Phinyo W., and Wiwat K. Low cost RISC implementation of intelligent ultra fast charger for Ni-Cd battery. Energy Convers Manage 49 2 (2007) 185-188
21. Vapnik V., Golowich S.E., and Smola A.J. Support vector method for function approximation, regression estimation and signal processing. Advanced neural information processing system (1996), MIT Press, Denver, CO, USA
22. Pai P.F., and Hong W.C. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms. Electric Power Syst Res 74 3 (2005) 417-419
23. Wang J.P., Chen Q.S., and Cao B.G. Support vector machine based battery model for electric vehicles. Energy Convers Manage 47 7-8 (2006) 858-7
24. Li X., Cao G.Y., and Zhu X.J. Modeling and control of PEMFC based on least squares support vector machines. Energy Convers Manage 47 7-8 (2006) 1032-19
25. Ying L.C., and Pan M.C. Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manage 49 2 (2008) 205-207
26. Dong B., Cao C., and Lee S.E. Applying support vector machines to predict building energy consumption in tropical region. Energy Build 37 5 (2005) 545-549
27. Vapnik V. The nature of statistical learning theory (1999), Springer, New York
28. DeST development group in Tsinghua University. Building Environmental System Simulation and Analysis-DeST. Beijing: China Architecture & Building Press; 2006.