Three Domain Modelling and Uncertainty Analysis (eBook)

Applications in Long Range Infrastructure Planning
eBook Download: PDF
2015 | 2015
XX, 206 Seiten
Springer International Publishing (Verlag)
978-3-319-19572-8 (ISBN)

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Three Domain Modelling and Uncertainty Analysis - Atom Mirakyan, Roland de Guio
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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.



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.

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.

Preface 7
Contents 9
List of Figures 13
List of Tables 15
Abbreviations and Symbols 17
1 Introduction 21
1.1 Scope and Structure of the Book 21
1.2 Main Questions Addressed and the Purpose of the Book 23
1.3 Overall Definitions and Theoretical Backgrounds 25
1.3.1 Defining Planning, Scenarios, Strategies and Initiatives 25
1.3.2 Systems from the System Science Point of View 28
1.3.3 Models and Modelling 30
1.3.4 Mixed Method Methodologies, a Pragmatic View 32
1.3.4.1 Introduction 32
1.3.4.2 Aspects for Designing Mixed Methods 33
1.3.5 Pre-existing Concepts of Uncertainty in Planning and Modelling 35
1.3.6 Planning and Decision Making in Different Information Availability Conditions 36
1.3.7 Theories for Uncertainty Analysis and Representation 37
1.3.7.1 Basic Notions of Probability Theory 37
1.3.7.2 Basic Notions of Fuzzy Set and Possibility Theory 38
1.3.7.3 Basic Notion of Evidence Theory 40
References 41
2 Energy Infrastructure Planning in Cities and Territories, Quality Factors of Methods for Infrastructure Planning 45
2.1 Introduction 45
2.2 Integrated Energy Planning in Cities and Territories 46
2.3 Energy Systems in City and Territory, a Sociotechnical Infrastructure 47
2.4 Defining Typology of Application or Use Cases 48
2.4.1 Use Case I: Decentralised Multi-model Based IEPCT 48
2.4.2 Use Case II: Integrated-Model Based IEPCT 49
2.5 Modelling in IEPCT 49
2.5.1 Models and Different Degrees of Formalisation 49
2.6 Overall Requirements and Quality Factors of Energy Planning and Modelling Methods 51
2.7 Summary and Open Problems 54
References 55
3 3-Domain Modelling 58
3.1 Introduction 58
3.2 3-Domain Metasystem 59
3.3 3-Domain Modelling: Different Approaches for Different Domains 62
3.3.1 Introduction 62
3.3.2 Data-Driven Modelling 63
3.3.3 Process-Driven Modelling 64
3.3.3.1 Comparison of Complex System Modelling Approaches 64
3.3.4 Judgmental-Driven Modelling 65
3.4 Defining Modelling Approaches for Different Modelling Domains and Use Cases 66
3.4.1 General 66
3.4.2 Modelling Approaches for Targeted Domain 67
3.4.2.1 Selecting the Modelling Methods 67
3.4.2.2 Selected Process Driven Models for Targeted Domain 67
System Dynamic (SD) Approach to Model the Targeted Domain in Use Case II—Mexico 67
3.4.2.3 Judgment-Driven Modelling Methods 69
MICMAC Approach to Model the Targeted Domain in Use Case I—Singapore 69
3.4.3 Data Driven Modelling Approaches for Neighbouring and Distant Domains 70
3.4.3.1 Selecting the Modelling Methods 70
3.4.3.2 Selected Data-Driven Modelling Methods 72
Linear Regression 72
Theta Model 73
ARIMA Models 73
Robust Trend and Random Walk 74
Artificial Neural Network (ANN) 75
S-shaped Curve Methods to Forecast Technology Evolution and Substitution 77
3.4.4 Modelling the Distant Domain and Its Impact to Other Domains 78
3.4.4.1 Reference Impact Matrix Method (RIM) 78
3.5 Summary of Modelling Approches for Different Use Cases and Domains 81
3.6 3-Domain Modelling in Context of Multi Method Research 82
References 82
4 Conceptual Basis of Uncertainty in IEPCT 86
4.1 Why Be Explicit About Uncertainty in IEPCT? 86
4.2 Typology of Uncertainty 87
4.2.1 Linguistic Uncertainty 88
4.2.2 Epistemic Uncertainty 88
4.2.3 Variability Uncertainty 89
4.2.4 Decision Making Uncertainty 89
4.2.5 Procedural Uncertainty 89
4.2.6 Levels of Uncertainty 90
4.3 Incorporating Uncertainty in Current IEPCT Studies 90
4.4 Conclusion 90
References 91
5 Multi-method Approaches for Uncertainty Analysis 92
5.1 Introduction 92
5.1.1 IEP in Cities and Territories, Specific Conditions 93
5.2 Analysis Sophistication Degrees 93
5.2.1 Introduction 93
5.2.2 Appropriate Analytical Degrees in IEPCT Context 95
5.3 Quality Factors of Methods for Uncertainty Analysis 96
5.3.1 Technical Quality Factors 96
5.3.2 Organisational Capability 96
5.3.3 Satisfaction by Planning Participants 97
5.4 Methods and Methodologies for Uncertainty Assessment: A Review 98
5.4.1 Evaluation Criteria 98
5.4.2 List of the Reviewed Methods and Methodologies 99
5.4.3 Summary of Evaluation Results of Reviewed Methods 99
5.5 Multi Method Approaches for Uncertainty Analysis 100
5.5.1 Introduction 100
5.5.2 Fuzzy Scenario Based Uncertainty Analysis for Use Case-I 100
5.5.2.1 Analysis Procedure and Steps, Functional View 100
5.5.2.2 Model Context Uncertainty Analysis 101
5.5.2.3 Methods for Model Structure Uncertainty Analysis 101
Method for the Analysis of Judgmental-Driven Model Structure Uncertainty in a Targeted Domain 101
Method for the Analysis of Model Structure Uncertainty for Data-Driven Models in Neighbouring or Distant Domain 103
Methods of Model Structure Uncertainty Analysis of Judgmental Driven Model in Distant Domain 103
5.5.2.4 Identification of Main Drivers (Key Descriptors) 104
5.5.2.5 Methods for Uncertainty Analysis of Models’ Inputs 104
Methods for the Uncertainty Analysis of Process-Driven Models’ Inputs in Targeted Domain 104
Methods for Uncertainty Analysis of Data-Driven Models’ Inputs in Neighbouring Domain 105
Methods for Uncertainty Analysis of Judgmental Driven Models’ Inputs in Distant Domain 105
5.5.2.6 Methods for Uncertainty Analysis of Model Outputs 106
Methods for Uncertainty Analysis of Process-Driven Models Output in Targeted Domain 106
Methods for Uncertainty Analysis of Data-Driven Models Outputs in Neighbouring Domain 106
Methods for the Uncertainty Analysis of Judgmental Driven Models Outputs in Distant Domain 107
5.5.2.7 Model Technical Uncertainty 107
5.5.2.8 Uncertainty Communication 107
5.5.2.9 Assignment FSUA Methods According Planning and Modelling Steps for Addressing Different Typologies of Uncertainties 108
5.5.3 Probabilistic, Random Sampling Based Uncertainty Analysis (PRSUA) Approach for Use Case-II 109
5.5.3.1 Analysis Procedure and Steps, Functional View 109
5.5.3.2 Model Context Uncertainty Analysis 109
5.5.3.3 Methods for Model Structure Uncertainty Analysis 111
Methods for the Analysis of Process-Driven Model Structure Uncertainty in a Targeted Domain 111
Methods for the Analysis of Model Structure Uncertainty for Data-Driven Models in Neighbouring or Distant Domain 112
Methods of Model Structure Uncertainty Analysis of Judgmental Driven Model in Distant Domain 112
5.5.3.4 Identification Main Model Drivers (Key Descriptors) 112
5.5.3.5 Methods for the Uncertainty Analysis of Models’ Inputs 113
Methods for the Uncertainty Analysis of Process-Driven Models’ Inputs in a Targeted Domain 113
Methods for Uncertainty Analysis of Data-Driven Models’ Inputs in Neighbouring Domain 113
Methods for Uncertainty Analysis of Judgmental Driven Models’ Inputs in Distant Domain 114
5.5.3.6 Methods for Uncertainty Analysis of Model Outputs 114
Methods for the Uncertainty Analysis of Process-Driven Model Outputs in Targeted Domain 114
Methods for the Uncertainty Analysis of Data-Driven Model Outputs in Neighbouring Domain 114
Methods for the Uncertainty Analysis of Judgmental Driven Model Outputs in the Distant Domain 115
5.5.3.7 Model Technical Uncertainty 115
5.5.3.8 Uncertainty Communication 115
5.5.3.9 Assignment PRSUA Methods According to Planning and Modelling Steps for Addressing Different Typologies of Uncertainty 116
5.6 A Review of Methods and Methodologies for Uncertainty Analysis 117
5.6.1 Correlations and Copulas 117
5.6.1.1 Description 117
5.6.1.2 Typology of Uncertainty Addressed 118
5.6.1.3 Potential, Main Rationales 118
5.6.1.4 Performance According to Some Quality Factors 118
Technical Quality Factors 118
Organisational Capability 118
Satisfaction by Planning Participants 119
5.6.1.5 Future Reading 119
5.6.2 Expert Elicitation 119
5.6.2.1 Description 119
5.6.2.2 Typology of Uncertainty Addressed 120
5.6.2.3 Potential, Main Rationales 120
5.6.2.4 Performance According to Quality Factors 120
Technical Quality Factors 120
Organisational Capability 120
Satisfaction by Planning Participants 120
5.6.2.5 Future Reading 121
5.6.3 Fuzzy Inference 121
5.6.3.1 Description 121
5.6.3.2 Potential, Main Rationales 122
5.6.3.3 Typology of Uncertainty Addressed 122
5.6.3.4 Performance According to Quality Factors 122
Technical Quality Factors 122
Organisational Capability 123
Satisfaction by Planning Participants 123
5.6.3.5 Future Reading 123
5.6.4 Innovative Multimethod Approach (IMMA) 123
5.6.4.1 Description 123
5.6.4.2 Typology of Uncertainty Addressed 124
5.6.4.3 Potential, Main Rationales 124
5.6.4.4 Performance According to Quality Factors 124
Technical Quality Factors 124
Organisational Capability 124
Satisfaction by Planning Participants 124
5.6.4.5 Future Reading 125
5.6.5 Inverse Modelling 125
5.6.5.1 Description 125
5.6.5.2 Potential, Main Rationales 125
5.6.5.3 Typology of Uncertainty Addressed 125
5.6.5.4 Performance According to Quality Factors 125
Technical Quality Factors 125
Organisational Capability 126
Satisfaction by Planning Participants 126
5.6.5.5 Future Reading 126
5.6.6 Interval Prediction (IP) in Data Driven Models 126
5.6.6.1 Description 126
5.6.6.2 Potential, Main Rationales 127
5.6.6.3 Typology of Uncertainty Addressed 128
5.6.6.4 Performance According to Quality Factors 128
Technical Quality Factors 128
Organisational Capability 128
Satisfaction by planning participants 128
5.6.6.5 Future Reading 129
5.6.7 Monte Carlo Simulation 129
5.6.7.1 Description 129
5.6.7.2 Potential, Main Rationales 129
5.6.7.3 Typology of Uncertainty Addressed 129
5.6.7.4 Performance According to Quality Factors 129
Technical Quality Factors 129
Organisational Capability 130
Satisfaction by Planning Participants 130
5.6.7.5 Future Reading 130
5.6.8 Multiple Model Simulation (MMS) of Process Driven Models 130
5.6.8.1 Description 130
5.6.8.2 Potential, Main Rationales 131
5.6.8.3 Typology of Uncertainty Addressed 131
5.6.8.4 Performance According to Some Quality Factors 131
Technical Quality Factors 131
Organisational Capability 131
Satisfaction by Planning Participants 132
5.6.8.5 Future Reading 132
5.6.9 Multiple Model Simulation (MMS) of Data Driven Models 132
5.6.9.1 Description 132
5.6.9.2 Potential, Main Rationales 133
5.6.9.3 Typology of Uncertainty Addressed 133
5.6.9.4 Performance According to Some Quality Factors 133
Technical quality factors 133
Organisational Capability 133
Satisfaction by planning participants 134
5.6.9.5 Future Reading 134
5.6.10 Scenario Analysis and Fuzzy Clustering 134
5.6.10.1 Description 134
Step 1 135
Step 2 135
Step 3 135
Step 4 135
Step 5 Scenario Selection 137
5.6.10.2 Potential, Main Rationales 139
5.6.10.3 Typology of Uncertainty Addressed 139
5.6.10.4 Performance According to Some Quality Factors 139
Technical Quality Factors 139
Organisational Capability 139
Satisfaction by Planning Participants 140
5.6.10.5 Future Reading 140
5.6.11 Sensitivity Analysis 140
5.6.11.1 Description 140
5.6.11.2 Potential, Main Rationales 141
5.6.11.3 Typology of Uncertainty Addressed 141
5.6.11.4 Performance According to Some Quality Factors 141
Technical Quality Factors 141
Organisational Capability 141
Satisfaction by Planning Participants 142
5.6.11.5 Future Reading 142
5.6.12 Tests of Complex Models for Model Uncertainty 142
5.6.12.1 Description 142
5.6.12.2 Potential, Main Rationales 143
5.6.12.3 Typology of Uncertainty Addressed 143
5.6.12.4 Performance According to Some Quality Factors 144
Technical Quality Factors 144
Organisational Capability 144
Satisfaction by Planning Participants 144
5.6.12.5 Future Reading 144
5.6.13 NUSAP and PRIMA Methodologies 144
5.6.13.1 Description 144
5.6.13.2 Potential, Main Rationales 145
5.6.13.3 Typology of Uncertainty Addressed 145
5.6.13.4 Performance According to Quality Factors 145
Technical Quality Factors 145
Organisational Capability 146
Satisfaction by Planning Participants 146
Future Reading 146
5.7 Summary 146
References 147
6 Implementation of Discussed Uncertainty Analysis Approaches in Case Studies 150
6.1 Selection of Application Studies 150
6.2 An Example of Use Case I: Singapore 151
6.2.1 Development of the “Singapore Sustainable Growth” Model 151
6.2.1.1 Historical and Current Situation 151
6.2.1.2 Modelling the Targeted Domain and Identification of Key Descriptors 152
6.2.1.3 Modelling the Neighbouring Domain 156
6.2.1.4 Modelling the Distant Domain 157
6.2.2 Uncertainty Analysis 157
6.2.2.1 Context and Framing Uncertainty Analysis 157
6.2.2.2 Model Structure Uncertainty Analysis 157
Model Structure Uncertainty in the Targeted Domain 157
Model Structure uncertainty in the Neighbouring Domain 161
Model Structure Uncertainty in the Distant Domain 161
6.2.2.3 Model Inputs Uncertainty Analysis 162
Model Inputs Uncertainty Analysis in the Targeted Domain 162
Uncertainty of Individual Model Driving Forces 162
Uncertainty Because of Interdependency Among Different Model Inputs and Linguistic Uncertainty 163
Model Inputs Uncertainty Analysis of the Neighbouring Domain 165
Model Inputs Uncertainty Analysis of the Distant Domain 166
6.2.2.4 Model Output Uncertainty 166
Model Output Uncertainty in the Neighbouring Domain 166
Model Output Uncertainty in the Targeted Domain 166
Model Output Uncertainty in the Distant Domain 171
6.3 An Example of Use Case II: Mexico City 171
6.3.1 Modelling Mexico City’s Waste-to-Energy System 171
6.3.1.1 The Waste Management System in Mexico City 172
6.3.1.2 Modelling the Targeted Domain and Identification of Key Descriptors 174
6.3.1.3 Modelling Neighbouring Domain 174
6.3.1.4 Modelling Distant Domain 174
6.3.2 Uncertainty Analysis 176
6.3.2.1 Context and Framing Uncertainty Analysis 176
6.3.2.2 Model Structure Uncertainty Analysis 176
Introduction 176
Model Structure Uncertainty Analysis in Targeted Domain 177
6.3.2.3 Model Inputs Uncertainty Analysis 178
Model Inputs Uncertainty Analysis of the Neighbouring Domain 178
Model Input Uncertainty Analysis in the Targeted Domain 178
Uncertainty of Individual Model Driving Forces 178
Uncertainty Because of Interdependency Among Different Model Inputs and Linguistic Uncertainty 178
Model Input Uncertainty Analysis in the Distant Domain 179
6.3.2.4 Model Output Uncertainty Analysis 179
Model Output Uncertainty Analysis in Neighbouring Domain 179
Model Output Uncertainty of Process Driven Models of the Targeted Domain 180
Model Output Uncertainty Analysis in Distant Domain 180
References 180
7 Evaluation and Discussion 182
7.1 Evaluation and Discussion of the 3-Domain Modelling Concept and Different Modelling Approaches 182
7.1.1 General 182
7.1.2 Modelling Approaches for Targeted Domain 183
7.1.2.1 Technical Quality 183
7.1.2.2 Organisational Capability 183
7.1.2.3 Satisfaction by Planning Participants 183
7.1.3 Modelling Approaches for Neighbouring Domain 184
7.1.3.1 Technical Quality 184
7.1.3.2 Organisational Capability 184
7.1.3.3 Satisfaction by Planning Participants 184
7.1.4 Modelling Approaches for Distant Domain 185
7.1.4.1 Technical Quality 185
7.1.4.2 Organisational Capability 185
7.1.4.3 Satisfaction by Planning Participants 185
7.2 Evaluation and Discussion of Uncertainty Analysis Approaches 185
7.2.1 General 185
7.2.2 Evaluation of FSUA Multi Method Approach and Discussion 186
7.2.2.1 Technical Quality of FSUA 186
7.2.2.2 Organisational Capability 187
7.2.2.3 Satisfaction by Planning Participants 187
7.2.3 Evaluation of PRSUA Multi Method Approach and Discussion 188
7.2.3.1 Technical Quality of PRSUA 188
7.2.3.2 Organisational Capability 189
7.2.3.3 Satisfaction by Planning Participants 189
7.2.4 Comparative Assessment of Proposed Approaches 191
References 191
8 Overall Conclusion and Future Research 192
8.1 Overall Synthesis and Conclusions 192
8.2 Synthesis and Conclusions of Chaps. < ExternalRef>
8.3 Synthesis and Conclusions of Chap. < ExternalRef>
8.4 Synthesis and Conclusion of Chap. < ExternalRef>
8.5 Synthesis and Conclusions of Chaps. < ExternalRef>
8.6 Future Work 196
Appendix ADescriptive Analysis, Modelling of Historical Data 197
Appendix BSome Empirical Results of Use Case I-Singapore 200
Appendix CSome Empirical Results of Use Case II-Mexico 210
Appendix DComparison Different Extrapolation, Data Driven Methods and Intervals 215
Index 221

Erscheint lt. Verlag 28.5.2015
Reihe/Serie Energy Systems
Zusatzinfo XX, 206 p. 90 illus., 41 illus. in color.
Verlagsort Cham
Sprache englisch
Themenwelt Mathematik / Informatik Mathematik
Technik
Wirtschaft Allgemeines / Lexika
Schlagworte Heterogeneity • Infrastructure planning in cities and territories • Long range infrastructure planning and modelling • Non-linear interactions • Scale multiplicity • Three domain modelling • uncertainty analysis • Uncertainty types in planning and modelling
ISBN-10 3-319-19572-7 / 3319195727
ISBN-13 978-3-319-19572-8 / 9783319195728
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