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

  • SHAOMEI YANG (a1) (a2) and QIAN ZHU (a1) (a2)


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.



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



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Mathematical Structures in Computer Science
  • ISSN: 0960-1295
  • EISSN: 1469-8072
  • URL: /core/journals/mathematical-structures-in-computer-science
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