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ISSN:2222-7059 (Print);EISSN: 2222-7067 (Online)
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Title : Model of forecasting public housing demand in the capital cities in China based on LS-SVMs
Author(s) : Ying Xie, Li Yang, Xiaohang Zhang
Author affiliation :
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Public housing construction has contributed a lot to the economic development in China. Based on the fact that the demand of public housing must be reasonably forecasted, which is an important basis to ensure the sustainable development of the economy, in order to optimize public investment and land resources, there are many uncertain factors to change the medium and long-term middle-lower income families. Considering public housing construction scale typically energy-intensive and resource-intensive activities, especially for the purpose of improving performance of public housing construction in China, in this paper, based on the national statistic data, characteristics of public housing demand have been analyzed. Because of the dynamic nature of public housing demand and the specificity of sample data, leading to forecasting of public housing demand very difficult, so a intelligent estimation model based on least squares support vector machines is presented to improve the forecasting process. The proposed model takes advantage of LS-SVMs ability to solve the problem with small samples and nonlinear regression. Furthermore, the proposed approach is shown more accurate for prediction in the case of real-word application.

Key words:Public housing , Demand, Forecasting

Cite it:
Ying Xie, Li Yang, Xiaohang Zhang, Model of forecasting public housing demand in the capital cities in China based on LS-SVMs,Advances in Industrial Engineering and Management, Vol.2, No.1, pp. 1-4, 2013

Full Text : PDF(size: 451.39 kB, pp.1-4, Download times:491)

DOI : 10.7508/AIEM-V2-N1-1-4

[1] Pan Yuhong, Shao Huilin, 2009. ‘An Outline of the Chongqing Construction Industry for Housing Industrialization’. International Conference on Management and Service Science , pp. 1-4.
[2] Hao Deng, Jin Xu & Hong Zeng. 2008, ‘Study on Measures and Strategies of Use Safety Management of Buildings in Longgang District of Shenzhen’. International Conference on Information Management, Innovation Management and Industrial Engineering, pp. 490-493.
[3] Shi, J.J., Zeng, S.X. & Tam, C.M. 1998. ‘Modeling and simulation of public housing construction in Hong Kong’, Simulation Conference Proceedings, pp. 1305-1310.
[4] Ibrahim Dincer& Marc A. Rosen. 1999, ‘Energy, environment and sustainable Development’, Applied Energy vol 64, pp. 427-440.
[5] Jun Li,Michel Colombier 2009. ‘Managing carbon emissions in China through building energy efficiency,’ Journal of Environmental Management vol 90, pp. 2436–2447.
[6] Cherry, S. 2007, ‘How to Build A Green City,’ Spectrum, IEEE , pp.26 - 29.
[7] Qian Shi & Shoufeng Chen. 2008, ‘Evaluation of Green Construction Alternatives Based on Value Engineering,’ WiCOM’08. 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp.1–6.
[8] Shang Chunjing, & Zhang Zhihui. ‘Building a New Concept System for Construction Quality Assessment of Life-cycle Carbon Emission for Buildings,’ Journal of Engineering Management, 2010, pp. 2–6.
[9] Suykens J A K, & Vandewalle J. 1999, ‘Least Squares Support Vector Machine Classifiers,’ Neural Processing Letter, vol. 9(3), pp: 293-300.
[10] Li Xie, Hua Zheng, Lizi Zhang, Dec. 2007, ‘Electricity price forecasting by clustering-LSSVM,’ Power Engineering Conference,IPEC 2007. International, , pp. 697-702.

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