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Eur J Oper Res ;— LCA of renewable energy for electricity generation sys- results with multiple methods, development of interactive deci- tems—A review. Life-cycle assessment for energy analysis and sion support systems and application of fuzzy methods to tackle management. Appl Energy ;— Life-cycle assessment in the renewable energy sector. Appl It seems that fuzzy theory, the use of more than one method in Energy ;— A guide to life-cycle greenhouse gas GHG emissions from the same application and the development of novel user-friendly electric supply technologies. Energy ;— Implications of Alternative Fuels and Technologies; First international Symposium on July , p.

Assessing renewables-to- electricity systems: a fuzzy expert system model.

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MCDA ;— Multiple [37] Weisser D. Costing electricity supply scenarios: a case study of promoting criteria decision analysis: state of the art surveys. United States of America: renewable energy technologies on Rodriguez, Mauritius. Renew Energy Springer; Climate change and renewable energy: how to choose the [38] Tremeac B, Meunier F. Life cycle analysis of 4.

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In: Ecological Economics. Sustainability in bines. Netherlands: Springer; Life cycle assessment of a wind farm and related externalities. Decision analysis in energy and environmental Renew Energy ;— Energy-economy models: a survey. Eur J Oper nologies. Transac Electr Electron Eng ;—8. Res ;— Modelling energy economic interactions in developping generation systems: a review. Renew Sustain Energy Rev ;—4. Life cycle energy requirements and greenhouse gas [6] Nijcamp P, Volwahsen A. New directions in integrated energy planning.

Energy Convers Manag Energy Policy ;18 8 — Application of multicriteria decision making [43] Hondo H. Life cycle GHG emission analysis of power generation systems: to sustainable energy planning-a review. Renew Sustain Energy Rev Japanese case.

Decision analysis applications in the operations eration options: The status of research in year Energy Policy research literature: — Oper Res ;— Perspective on decision analysis appli- [45] Mahapatra S, Dasappa S. Decision Anal ;1 1 :5— Decision analysis in energy and environmental Dev ;— Sustainable pv systems in buildings: present situation and future trends. Energ Policy ;— Renew Energ Renew Energy ;— Energy Convers Manag ;— Criteria Analysis.

Green Energy and Technology. London Springer; Energy Policy ;— WCoEaD, our common future. Renew Sustain Energy Rev shore wind energy.


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Clean — Techn Environ Policy ;—6. Selection of appropriate greenhouse gas mitigation options.

Multi Criteria Analysis in the Renewable Energy Industry

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Design of electricity tariff plans using gap statistic for K-means clustering based on consumers monthly electricity consumption data. Energy management techniques.

California's Renewable Energy Problem

Towards a process of agenda setting driven by social media. An ideal and a negative-ideal solutions are formed. The ideal solution is formed as a composite of the best performance value exhibited by any alternative for each attribute and the negative-ideal solution is the composite of the worst performance values. The chosen alternative should be as close to the ideal solution as possible and as far from the negative-ideal solution as possible. In this chapter, the method is applied to the selection of a renewable project for electric generation.

To estimate the efficiency scores of companies, various methods have been developed during the past two decades. These methods are generally classified as parametric and non-parametric methods.

Multi Criteria Analysis in the Renewable Energy Industry | zopusalawyky.ga

In the parametric methods, a cost of production function is estimated, whereas in the non-parametric methods, it is not necessary to estimate the cost or production function. Utility theory has generally been used to develop a relationship between utility and costs incurred as a consequence of a particular decision. These kinds of criteria make the evaluation process hard and vague. To deal with vagueness of human thought Zadeh [ 1 ] first introduced the fuzzy set theory, which was oriented to the rationality of uncertainty due to imprecision or vagueness. A major contribution of fuzzy set theory is its capability of representing vague data.

In a classical set, an element belongs to, or does not belong to, a set whereas an element of a fuzzy set naturally belongs to the set with a membership value from the interval [0,1]. That is, the performance of alternative solutions can be determined only approximately and therefore is introduced into the calculations as a fuzzy number.