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001 978-1-4471-5143-2
003 DE-He213
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007 cr nn 008mamaa
008 130806s2013 xxk| s |||| 0|eng d
020 _a9781447151432
_9978-1-4471-5143-2
024 7 _a10.1007/978-1-4471-5143-2
_2doi
050 4 _aTJ807-830
072 7 _aTHX
_2bicssc
072 7 _aSCI024000
_2bisacsh
082 0 4 _a621.042
_223
100 1 _aCavallaro, Fausto.
_eeditor.
245 1 0 _aAssessment and Simulation Tools for Sustainable Energy Systems
_h[electronic resource] :
_bTheory and Applications /
_cedited by Fausto Cavallaro.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2013.
300 _aXXV, 427 p. 98 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aGreen Energy and Technology,
_x1865-3529 ;
_v129
505 0 _aPart I -- 1.Sustainability assessment of solar technologies based on linguistic information -- 2.Photovoltaic plants selection on an insular grid using multicriteria outranking tools: application in Corsica island (France) -- 3.Assessment of Green Energy Alternatives Using Fuzzy ANP -- 4.Decision Criteria for Optimal Location of Solar Plants: Photovoltaic and Thermoelectric -- 5.A Multi-Attribute Model for Wind Farm location combining Cloud and Utility Theories -- 6.Territorial Design for Matching Green Energy Supply and Energy Consumption: The Case of Turkey -- 7.A cumulative belief degree approach for prioritization of energy sources: Case of Turkey -- 8.MCDA: Measuring robustness as a tool to address strategic wind farms issues -- 9.Assessment of Energy Efficiency Technologies: Case of Heat Pump Water Heaters -- Part II -- 10.A Fuzzy Paradigm for the Sustainability Evaluation of Energy Systems -- 11.Artificial Neural Networks and Genetic Algorithms for the Modelling, Simulation and Performance Prediction of Solar Energy Systems -- 12.Artificial Neural Network based methodologies for the estimation of wind speed -- 13.The use of Genetic Algorithms to solve the allocation problems in the Life Cycle Inventory -- 14.Design and implementation of maximum power point tracking algorithm using fuzzy logic and genetic algorithm -- 15.Chapter 15 Simulation and Renewable Energy Systems -- 16.Combining Mathematical Programming and Monte Carlo simulation to deal with uncertainty in energy project portfolio selection -- 17.Value Stream Maps for Industrial Energy Efficiency -- 18.Assessment of Energy Efficiency in Lean Transformation: A Simulation Based Improvement Methodology -- 19.Socio-Effective Value of Bio-Diesel Production.
520 _aIn recent years the concept of energy has been revised and a new model based on the principle of sustainability has become more and more pervasive. The appraisal of energy technologies and projects is complex and uncertain as the related decision making has to encompass environmental, technical, economic and social factors and information sources. The scientific procedure of assessment has a vital role as it can supply the right tools to evaluate the actual situation and make realistic forecasts of the effects and outcomes of any actions undertaken. Assessment and Simulation Tools for Sustainable Energy Systems offers reviews of the main assessment and simulation methods used for effective energy assessment.   Divided across three sections, Assessment and Simulation Tools for Sustainable Energy Systems develops the reader’s ability to select suitable tools to support decision making and implementation of sustainable energy projects. The first is dedicated to the analysis of theoretical foundations and applications of multi-criteria decision making. This is followed by chapters concentrating on the theory and practice of fuzzy inference, neural nets and algorithms genetics. Finally, simulation methods such as Monte Carlo analysis, mathematical programming and others are detailed.   This comprehensive illustration of these tools and their application makes Assessment and Simulation Tools for Sustainable Energy Systems a key guide for researchers, scientists, managers, politicians and industry professionals  developing the field of sustainable energy systems. It may also prompt further advancements in soft computing and simulation issues for students and researchers.
650 0 _aComputer simulation.
650 0 _aRenewable energy sources.
650 1 4 _aEnergy.
650 2 4 _aRenewable and Green Energy.
650 2 4 _aRenewable and Green Energy.
650 2 4 _aSimulation and Modeling.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447151425
830 0 _aGreen Energy and Technology,
_x1865-3529 ;
_v129
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-5143-2
912 _aZDB-2-ENE
999 _c94794
_d94794