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Tools and Methods of Information Support for the Development of Renewable Energy

Tools and Methods of Information Support for the Development of Renewable Energy

Ravil I. Mukhamediyev, Sophia Kiseleva, Kirill Yakunin, Renat Mustakayev, Jelena Muhamedijeva
Copyright: © 2019 |Pages: 27
ISBN13: 9781522591795|ISBN10: 1522591796|ISBN13 Softcover: 9781522591801|EISBN13: 9781522591818
DOI: 10.4018/978-1-5225-9179-5.ch002
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MLA

Mukhamediyev, Ravil I., et al. "Tools and Methods of Information Support for the Development of Renewable Energy." Renewable Energy and Power Supply Challenges for Rural Regions, edited by Valeriy Kharchenko and Pandian Vasant, IGI Global, 2019, pp. 39-65. https://doi.org/10.4018/978-1-5225-9179-5.ch002

APA

Mukhamediyev, R. I., Kiseleva, S., Yakunin, K., Mustakayev, R., & Muhamedijeva, J. (2019). Tools and Methods of Information Support for the Development of Renewable Energy. In V. Kharchenko & P. Vasant (Eds.), Renewable Energy and Power Supply Challenges for Rural Regions (pp. 39-65). IGI Global. https://doi.org/10.4018/978-1-5225-9179-5.ch002

Chicago

Mukhamediyev, Ravil I., et al. "Tools and Methods of Information Support for the Development of Renewable Energy." In Renewable Energy and Power Supply Challenges for Rural Regions, edited by Valeriy Kharchenko and Pandian Vasant, 39-65. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-9179-5.ch002

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Abstract

The chapter considers information systems that provide analysis of the RES resource base and support decision making. RES resources in the Republic of Kazakhstan are briefly analyzed. The methods and systems for evaluation of theoretical, technical, and economic potential of renewable energy sources are considered. Based on this overview, the authors propose six steps of estimation that should be realized in intellectual GIS. One of the major steps is the identification the factors that affect using of renewable energy sources. These factors can contribute to impeding the development of green energy. The simple taxonomy of these factors is proposed. The second important step is to aggregate estimations of the factors. As a result, the authors propose the aggregation method based on Bayes rule. Compared to other methods, this approach allows us to obtain two estimates: probabilities of positive and negative hypotheses. The methods discussed are at the heart of the information system supporting the development of renewable energy sources in the Republic of Kazakhstan.

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