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Development of the location suitability index for wave energy production by ANN and MCDM techniques

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  • Ghosh, Soumya
  • Chakraborty, Tilottama
  • Saha, Satyabrata
  • Majumder, Mrinmoy
  • Pal, Manish

Abstract

The exigency of energy demand from the overgrowth in the population density worldwide has prompted many countries to seek alternative renewable sources of energy. Countries with coastal regions have the advantage of the ability to utilize an extra source of renewable energy, that is, the energy from ocean waves. However, one of the major problems with wave energy is that it is location dependent, where many factors that increase its utilization potential are dependent on the feasibility of the locations. There is a lack of objective, relative and cognitive methods for the estimation of location suitability for ocean wave energy production. The present study attempts to address this lacuna and proposes a new method which is both objective and cognitive to identify suitable locations where the optimal amount of wave energy can be produced. The proposed method used the MCDM cascaded to ANN techniques to predict an index that directly represents the suitability of locations for wave energy generation. The index was applied to two different locations with varied levels of wave energy potential. The results encourage the authors for further application of the method.

Suggested Citation

  • Ghosh, Soumya & Chakraborty, Tilottama & Saha, Satyabrata & Majumder, Mrinmoy & Pal, Manish, 2016. "Development of the location suitability index for wave energy production by ANN and MCDM techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1017-1028.
  • Handle: RePEc:eee:rensus:v:59:y:2016:i:c:p:1017-1028
    DOI: 10.1016/j.rser.2015.12.275
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    8. Vasileiou, Margarita & Loukogeorgaki, Eva & Vagiona, Dimitra G., 2017. "GIS-based multi-criteria decision analysis for site selection of hybrid offshore wind and wave energy systems in Greece," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 745-757.
    9. Şan, Murat & Akpınar, Adem & Bingölbali, Bilal & Kankal, Murat, 2021. "Geo-spatial multi-criteria evaluation of wave energy exploitation in a semi-enclosed sea," Energy, Elsevier, vol. 214(C).
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    11. Foteinis, S. & Tsoutsos, T., 2017. "Strategies to improve sustainability and offset the initial high capital expenditure of wave energy converters (WECs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 775-785.
    12. Cucchiella, Federica & D’Adamo, Idiano & Gastaldi, Massimo & Koh, SC Lenny & Rosa, Paolo, 2017. "A comparison of environmental and energetic performance of European countries: A sustainability index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 401-413.
    13. Pınar Kaya Samut, 2017. "Integrated FANP-f-MIGP model for supplier selection in the renewable energy sector," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(3), pages 427-450, May.
    14. Alizadeh, Reza & Soltanisehat, Leili & Lund, Peter D. & Zamanisabzi, Hamed, 2020. "Improving renewable energy policy planning and decision-making through a hybrid MCDM method," Energy Policy, Elsevier, vol. 137(C).
    15. Shao, Meng & Han, Zhixin & Sun, Jinwei & Xiao, Chengsi & Zhang, Shulei & Zhao, Yuanxu, 2020. "A review of multi-criteria decision making applications for renewable energy site selection," Renewable Energy, Elsevier, vol. 157(C), pages 377-403.
    16. Kumar, Abhishek & Sah, Bikash & Singh, Arvind R. & Deng, Yan & He, Xiangning & Kumar, Praveen & Bansal, R.C., 2017. "A review of multi criteria decision making (MCDM) towards sustainable renewable energy development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 596-609.
    17. Tilottama Chakraborty & Mrinmoy Majumder, 2019. "Application of statistical charts, multi-criteria decision making and polynomial neural networks in monitoring energy utilization of wave energy converters," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 21(1), pages 199-219, February.
    18. Zheng, Chong-wei, 2021. "Global oceanic wave energy resource dataset—with the Maritime Silk Road as a case study," Renewable Energy, Elsevier, vol. 169(C), pages 843-854.
    19. Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
    20. Zheng, Chong Wei & Wang, Qing & Li, Chong Yin, 2017. "An overview of medium- to long-term predictions of global wave energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1492-1502.
    21. Soumya Ghosh & Mrinmoy Majumder & Manish Pal, 2018. "Application of metaheuristic algorithm to identify priority parameters for the selection of feasible location having optimum wave energy potential," Energy & Environment, , vol. 29(1), pages 3-28, February.

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