Renewable Energy Investment Decision Evaluation for Local Authorities - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2022

Renewable Energy Investment Decision Evaluation for Local Authorities

Résumé

Currently, increasing urban population creates escalating problems such as housing, infrastructure, transportation, health, environment, safety, and energy consumption. Climate change, emission mitigation, and limited energy supply force urban managers to consider sound measures with the support of technological developments. It is based on data collection and accumulation using IoT, sensors, digital networks, and other means. “Smart urbanism” is a concept that considers predicting, designing, and creating solutions in a systematic, sustainable, and agile manner based on the data collected. Energy is an indispensable dimension in this context. Optimum energy management makes it “smart energy” with the inclusion clean and sustainable renewable energy resources as well as energy efficiency. This study is performed to analyze possible inclusion of geothermal, solar, and wind power. The analyses are based to reveal the best feasible alternative considering the parameters of location, climate, space availability, capital and operational expenditures as well as construction, operation, and maintenance. The Fuzzy Analytic Hierarchy Process (Fussy AHP) technique is used to evaluate the ranking. The proposed method uses fuzzy mathematics for solving problems containing uncertainties as well as less quantifiable. This study proposes a methodological framework for the analysis of competitiveness of alternative renewable energy generations in urban environments. The municipality of Balıkesir is chosen for the case study presented in this work.
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Dates et versions

hal-04120821 , version 1 (07-06-2023)

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Ecem Turkoglu, Uner Colak, Gulgun Kayakutlu, Irem Duzdar Argun. Renewable Energy Investment Decision Evaluation for Local Authorities. IFIP International Workshop on Artificial Intelligence for Knowledge Management (AI4KMES), Aug 2021, Montreal, QC, Canada. pp.197-214, ⟨10.1007/978-3-030-96592-1_15⟩. ⟨hal-04120821⟩
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