Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-25T23:29:25.431Z Has data issue: false hasContentIssue false
  • English
  • Français

The GA-SA model and its application to predicting the potential of the solar power industry†

Published online by Cambridge University Press:  04 September 2014

SHAOMEI YANG  and
QIAN ZHU
SHAOMEI YANG
Affiliation:
Economics and Management Department, North China Electric Power University, Baoding, China Department of Economics and Trade, Hebei Finance University, Baoding, China Email: [email protected]; [email protected]
QIAN ZHU
Affiliation:
Economics and Management Department, North China Electric Power University, Baoding, China Department of Economics and Trade, Hebei Finance University, Baoding, China Email: [email protected]; [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

In recent years, under the dual pressure of environmental requirements and a series of conventional energy shortages, including power cuts, coal shortages and rising oil prices, there have been unprecedented opportunities for clean energy, and especially for the development and utilisation of solar energy. Hence, solar products have become increasingly popular because of the energy saving and environmental protection they offer. China's solar energy industry should be in the self-development mechanism, which is market-oriented and should act as a mainstay for enterprises. Scientifically forecasting the potential of the solar energy industry and rationally evaluating its status as a result of a market economy-oriented development is an effective means of building a low-carbon and harmonious society. In the work reported in this paper, we:

  • – established a comprehensive evaluation index system, covering natural resources, economic conditions, policy support, technology and the market environment;

  • – constructed a GA-SA model based on analysing the principles of GA (genetic algorithms) and SA (simulated annealing); and

  • – applied these tools to predicting the potential of the solar power industry.

The results show that GA-SA takes into account both global and local search issues, and is thus a complete optimisation method, and that the model also has scientific and broad applicability in the field of prediction.

Type
Paper
Information
Mathematical Structures in Computer Science , Volume 24 , Special Issue 5: Mathematical Method in Intelligent Computing and Automation , October 2014 , e240512
Copyright
Copyright © Cambridge University Press 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

†

This work was supported by ‘the Fundamental Research Funds for the Central Universities’ (12MS134).

References

Bai, X., Lv, X. and Sun, J. (2006) Application of Genetic Algorithm Fusing Simulated Annealing in Document Clustering. Computer Engineering and Applications 23 (2)144–148.Google Scholar
He, Z.-G. and Chen, S.-H. (2004) Application of mixed genetic-simulated annealing algorithms to slope stability analysis. Rock and Soil Mechanics 27 (2)317–319.Google Scholar
Lai, H., Dong, P. and Zhu, G. (2004) Application of BP Networks Based on GASA Hybrid Strategy to Measuring and Calculating Base Land Price. Geomatics and Information Science of Wuhan University 24–28.Google Scholar
Li, J.-H., Zhou, T.-R. and Zheng, R. (2005) SA-improved GA and its Application. Journal of Nanchang University (Natural Sciences) 12 (3)397–390.Google Scholar
Lu, J., Zeng, F. and Sun, Q. (2007) Prediction of Sales Based on Simulated Annealing Algorithm and Genetic Algorithm Optimised Neural Networks. Journal of Lanzhou Jiaotong University(Natural Sciences) 17 (6)113–115.Google Scholar
Sang, J., Liu, Y. and Lin, L. (2006) Characteristic of Solar Radiation in Ningxia and Integrated Evaluation on Utilization Potential of Solar Energy. Journal of Desert Research 10 (1)122–125.Google Scholar
Tian, J. and Gao, M. (2006) The research and application on artificial neural network algorithm, Beijing institute of technology press 19 (6) 41–87.Google Scholar
Wang, W., Cai, Z.-L. and Chen, J. (2009) Application of SAGA in Optimising Dangerous Freight Loading. Journal of Xuchang University 17 (9)31–33.Google Scholar
Wei, W. (2010) Illuminate the future power, China's solar energy industry. East China Science and Technology 7 (1)68–76.Google Scholar
Xiang, C., Zhou, J. and Zhou, Z. (2010) Application Research on Biology Multiple Sequence Alignment Based on Simulated Annealing Genetic Algorithm. Hunan Agricultural Science 4 (1)29–31.Google Scholar
You, Y. (2007) Application of the Annealing and Genetic Algorithm in the Calibration of Airborne Particle Counter. Journal Of Taiyuan Normal University (Natural Science Edition) 20 (9)69–74.Google Scholar
Zhang, B. and Hao, Y. (2009) Annealing Genetic Algorithm and Its Application in One-dimensional Cutting Stock Problem. Journal of Xuchang University 19 (1)63–65.Google Scholar