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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: yangshaomei77@126.com; year1212@126.com
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: yangshaomei77@126.com; year1212@126.com
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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 

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Footnotes

†

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

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