Achieving SDG 7, universal access to affordable, reliable, and clean energy, is vital for tackling the energy crisis and ensuring sustainable development. In Uttarakhand, despite rich renewable potential, energy access is hindered by challenging terrain, ecological sensitivity, and infrastructure gaps. This research proposes a hybrid decision-making framework combining Machine Learning (ML) with Hesitant Fuzzy Multi-Criteria Decision-Making (MCDM) to identify the best renewable energy options for the region. Five alternatives, solar PV, solar thermal, CSP, mini & small hydropower, and bioenergy, were chosen based on resources and expert input. Using bibliometric analysis in R and the Nominal Group Technique (NGT), criteria were set. The Hesitant Fuzzy AHP assigned weights, while H-FTOPSIS ranked options. Logistic regression enhanced prediction accuracy, and sensitivity analysis tested model stability. Results show solar PV as the most viable choice. The framework supports strategic, evidence-based energy planning for Uttarakhand and offers a scalable, adaptable method for similar regions worldwide.
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Paperback. Etat : new. Paperback. Achieving SDG 7, universal access to affordable, reliable, and clean energy, is vital for tackling the energy crisis and ensuring sustainable development. In Uttarakhand, despite rich renewable potential, energy access is hindered by challenging terrain, ecological sensitivity, and infrastructure gaps. This research proposes a hybrid decision-making framework combining Machine Learning (ML) with Hesitant Fuzzy Multi-Criteria Decision-Making (MCDM) to identify the best renewable energy options for the region. Five alternatives, solar PV, solar thermal, CSP, mini & small hydropower, and bioenergy, were chosen based on resources and expert input. Using bibliometric analysis in R and the Nominal Group Technique (NGT), criteria were set. The Hesitant Fuzzy AHP assigned weights, while H-FTOPSIS ranked options. Logistic regression enhanced prediction accuracy, and sensitivity analysis tested model stability. Results show solar PV as the most viable choice. The framework supports strategic, evidence-based energy planning for Uttarakhand and offers a scalable, adaptable method for similar regions worldwide. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N° de réf. du vendeur 9786208437046
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Taschenbuch. Etat : Neu. A Renewable Energy Selection Model for Sustainable Development | Integrating Hesitant Fuzzy Logic and Machine Learning for Renewable Energy Optimization | Virendra Singh Rana (u. a.) | Taschenbuch | Englisch | 2025 | LAP LAMBERT Academic Publishing | EAN 9786208437046 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. N° de réf. du vendeur 134443050
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Achieving SDG 7, universal access to affordable, reliable, and clean energy, is vital for tackling the energy crisis and ensuring sustainable development. In Uttarakhand, despite rich renewable potential, energy access is hindered by challenging terrain, ecological sensitivity, and infrastructure gaps. This research proposes a hybrid decision-making framework combining Machine Learning (ML) with Hesitant Fuzzy Multi-Criteria Decision-Making (MCDM) to identify the best renewable energy options for the region. Five alternatives, solar PV, solar thermal, CSP, mini & small hydropower, and bioenergy, were chosen based on resources and expert input. Using bibliometric analysis in R and the Nominal Group Technique (NGT), criteria were set. The Hesitant Fuzzy AHP assigned weights, while H-FTOPSIS ranked options. Logistic regression enhanced prediction accuracy, and sensitivity analysis tested model stability. Results show solar PV as the most viable choice. The framework supports strategic, evidence-based energy planning for Uttarakhand and offers a scalable, adaptable method for similar regions worldwide.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 228 pp. Englisch. N° de réf. du vendeur 9786208437046
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