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E-raamat: Three Domain Modelling and Uncertainty Analysis: Applications in Long Range Infrastructure Planning

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  • Formaat: PDF+DRM
  • Sari: Energy Systems
  • Ilmumisaeg: 28-May-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319195728
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Three Domain Modelling and Uncertainty Analysis: Applications in Long Range Infrastructure PlanningSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Energy Systems
  • Ilmumisaeg: 28-May-2015
  • Kirjastus: Springer International Publishing AG
  • Keel: eng
  • ISBN-13: 9783319195728
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This book examines in detail the planning and modelling of local infrastructure like energy systems, including the complexities resulting from various uncertainties. Readers will discover the individual steps involved in infrastructure planning in cities and territories, as well as the primary requirements and supporting quality factors. Further topics covered concern the field of uncertainty and its synergies with infrastructure planning. Theories, methodological backgrounds and concrete case studies will not only help readers to understand the proposed methodologies for modelling and uncertainty analysis, but will also show them how these approaches are implemented in practice.

1 Introduction
1(24)
1.1 Scope and Structure of the Book
1(2)
1.2 Main Questions Addressed and the Purpose of the Book
3(2)
1.3 Overall Definitions and Theoretical Backgrounds
5(20)
1.3.1 Defining Planning, Scenarios, Strategies and Initiatives
5(3)
1.3.2 Systems from the System Science Point of View
8(2)
1.3.3 Models and Modelling
10(2)
1.3.4 Mixed Method Methodologies, a Pragmatic View
12(3)
1.3.5 Pre-existing Concepts of Uncertainty in Planning and Modelling
15(1)
1.3.6 Planning and Decision Making in Different Information Availability Conditions
16(1)
1.3.7 Theories for Uncertainty Analysis and Representation
17(4)
References
21(4)
2 Energy Infrastructure Planning in Cities and Territories, Quality Factors of Methods for Infrastructure Planning
25(14)
2.1 Introduction
25(1)
2.2 Integrated Energy Planning in Cities and Territories
26(1)
2.3 Energy Systems in City and Territory, a Sociotechnical Infrastructure
27(1)
2.4 Defining Typology of Application or Use Cases
28(1)
2.4.1 Use Case I: Decentralised Multi-model Based IEPCT
28(1)
2.4.2 Use Case II: Integrated-Model Based IEPCT
29(1)
2.5 Modelling in IEPCT
29(2)
2.5.1 Models and Different Degrees of Formalisation
29(2)
2.6 Overall Requirements and Quality Factors of Energy Planning and Modelling Methods
31(3)
2.7 Summary and Open Problems
34(5)
References
35(4)
3 3-Domain Modelling
39(28)
3.1 Introduction
39(1)
3.2 3-Domain Metasystem
40(3)
3.3 3-Domain Modelling: Different Approaches for Different Domains
43(4)
3.3.1 Introduction
43(1)
3.3.2 Data-Driven Modelling
44(1)
3.3.3 Process-Driven Modelling
45(1)
3.3.4 Judgmental-Driven Modelling
46(1)
3.4 Defining Modelling Approaches for Different Modelling Domains and Use Cases
47(15)
3.4.1 General
47(1)
3.4.2 Modelling Approaches for Targeted Domain
48(3)
3.4.3 Data Driven Modelling Approaches for Neighbouring and Distant Domains
51(8)
3.4.4 Modelling the Distant Domain and Its Impact to Other Domains
59(3)
3.5 Summary of Modelling Approches for Different Use Cases and Domains
62(1)
3.6 3-Domain Modelling in Context of Multi Method Research
63(4)
References
63(4)
4 Conceptual Basis of Uncertainty in IEPCT
67(6)
4.1 Why Be Explicit About Uncertainty in IEPCT?
67(1)
4.2 Typology of Uncertainty
68(3)
4.2.1 Linguistic Uncertainty
69(1)
4.2.2 Epistemic Uncertainty
69(1)
4.2.3 Variability Uncertainty
70(1)
4.2.4 Decision Making Uncertainty
70(1)
4.2.5 Procedural Uncertainty
70(1)
4.2.6 Levels of Uncertainty
71(1)
4.3 Incorporating Uncertainty in Current IEPCT Studies
71(1)
4.4 Conclusion
71(2)
References
72(1)
5 Multi-method Approaches for Uncertainty Analysis
73(58)
5.1 Introduction
73(1)
5.1.1 IEP in Cities and Territories, Specific Conditions
74(1)
5.2 Analysis Sophistication Degrees
74(3)
5.2.1 Introduction
74(2)
5.2.2 Appropriate Analytical Degrees in IEPCT Context
76(1)
5.3 Quality Factors of Methods for Uncertainty Analysis
77(2)
5.3.1 Technical Quality Factors
77(1)
5.3.2 Organisational Capability
77(1)
5.3.3 Satisfaction by Planning Participants
78(1)
5.4 Methods and Methodologies for Uncertainty Assessment: A Review
79(2)
5.4.1 Evaluation Criteria
79(1)
5.4.2 List of the Reviewed Methods and Methodologies
80(1)
5.4.3 Summary of Evaluation Results of Reviewed Methods
80(1)
5.5 Multi Method Approaches for Uncertainty Analysis
81(17)
5.5.1 Introduction
81(1)
5.5.2 Fuzzy Scenario Based Uncertainty Analysis for Use Case-I
81(9)
5.5.3 Probabilistic, Random Sampling Based Uncertainty Analysis (PRSUA) Approach for Use Case-II
90(8)
5.6 A Review of Methods and Methodologies for Uncertainty Analysis
98(29)
5.6.1 Correlations and Copulas
98(2)
5.6.2 Expert Elicitation
100(2)
5.6.3 Fuzzy Inference
102(2)
5.6.4 Innovative Multimethod Approach (IMMA)
104(2)
5.6.5 Inverse Modelling
106(1)
5.6.6 Interval Prediction (IP) in Data Driven Models
107(3)
5.6.7 Monte Carlo Simulation
110(1)
5.6.8 Multiple Model Simulation (MMS) of Process Driven Models
111(2)
5.6.9 Multiple Model Simulation (MMS) of Data Driven Models
113(2)
5.6.10 Scenario Analysis and Fuzzy Clustering
115(6)
5.6.11 Sensitivity Analysis
121(2)
5.6.12 Tests of Complex Models for Model Uncertainty
123(2)
5.6.13 NUSAP and PRIMA Methodologies
125(2)
5.7 Summary
127(4)
References
128(3)
6 Implementation of Discussed Uncertainty Analysis Approaches in Case Studies
131(32)
6.1 Selection of Application Studies
131(1)
6.2 An Example of Use Case I: Singapore
132(20)
6.2.1 Development of the "Singapore Sustainable
Growth" Model
132(6)
6.2.2 Uncertainty Analysis
138(14)
6.3 An Example of Use Case II: Mexico City
152(11)
6.3.1 Modelling Mexico City's Waste-to-Energy System
152(5)
6.3.2 Uncertainty Analysis
157(4)
References
161(2)
7 Evaluation and Discussion
163(10)
7.1 Evaluation and Discussion of the 3-Domain Modelling Concept and Different Modelling Approaches
163(3)
7.1.1 General
163(1)
7.1.2 Modelling Approaches for Targeted Domain
164(1)
7.1.3 Modelling Approaches for Neighbouring Domain
165(1)
7.1.4 Modelling Approaches for Distant Domain
166(1)
7.2 Evaluation and Discussion of Uncertainty Analysis Approaches
166(7)
7.2.1 General
166(1)
7.2.2 Evaluation of FSUA Multi Method Approach and Discussion
167(2)
7.2.3 Evaluation of PRSUA Multi Method Approach and Discussion
169(3)
7.2.4 Comparative Assessment of Proposed Approaches
172(1)
References
172(1)
8 Overall Conclusion and Future Research
173(6)
8.1 Overall Synthesis and Conclusions
173(1)
8.2 Synthesis and Conclusions of Chaps. 1 and 2
173(1)
8.3 Synthesis and Conclusions of Chap. 3
174(1)
8.4 Synthesis and Conclusion of Chap. 4
175(1)
8.5 Synthesis and Conclusions of Chaps. 5, 6 and 7
175(2)
8.6 Future Work
177(2)
Appendix A Descriptive Analysis, Modelling of Historical Data 179(4)
Appendix B Some Empirical Results of Use Case I-Singapore 183(10)
Appendix C Some Empirical Results of Use Case II-Mexico 193(6)
Appendix D Comparison Different Extrapolation, Data Driven Methods and Intervals 199(6)
Index 205
Atom Mirakyan studied engineering at the Technical University in Erevan/Armenia (Dipl.-Ing.) and Energy economics (Dipl.-Energy economics) at University of apply science in Darmstadt/Germany. He works at Technical University in Darmstadt as scientist in the field of energy planning and modelling for 5 years. As energy consultant he does energy planning and regional development consultancy for cities and territories for 4 years. In 2007 he joined the European Institute for Energy Research working on energy planning and modelling. His research focus is techno-economic and ecological modelling and planning of energy systems, uncertainty analysis and life cycle assessment. He has also developed methods for innovative support of planning and system design. He has done his PhD about Methodological frameworks for uncertainty analysis in long range integrated energy planning for cities and territories (IEPCT) at University of Strasbourg in 2014. In his PhD frame developed uncertainty analysis approaches have been successfully implemented in megacity studies, in context of energy planning and modelling.

Roland De Guio is professor of Industrial and Systems Engineering at I.N.S.A Graduate School of Science and Technology, Strasbourg France. Since 2000, he manages research activities about applications of theory of inventive problem solving on technical and non-technical multidisciplinary problems. Among his activities he worked on long run technological forecast since 2004 and started his collaboration with EIFER in the area of energy planning in 2010.
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