Decision Making Using AI in Energy and Sustainability: Methods and Models for Policy and Practice

Foreword
Preface
Contents
Part I: Sustainability Policies
	Chapter 1: Climate Change - Can AI Help Understanding and More Effective Facing of Various Interrelated Impacts?
		1.1 Introduction
		1.2 Complexity of Climate Change: Comprehension of the Influencing Factors
			1.2.1 Influencing Factors
				1.2.1.1 Globalization
				1.2.1.2 Innovation and Technology
			1.2.2 Models, Components, Relations
		1.3 Some Initiatives
		1.4 Evaluation of Impacts
			1.4.1 Simulation
			1.4.2 Assessment
		1.5 Conclusion and Perspective
		References
	Chapter 2: The European Green Deal and the 17 SDGs: Uncovering their Connection with a ML-based Approach
		2.1 Introduction
			2.1.1 The European Green Deal (EGD)
			2.1.2 Energy-Related Policies Derived from the EGD
				2.1.2.1 A New Industrial Strategy for Europe
				2.1.2.2 EU Hydrogen Strategy
				2.1.2.3 The Annual Sustainable Growth Strategy of 2021 (7 Technology Flagship Areas)
				2.1.2.4 Chemicals Strategy for Sustainability
				2.1.2.5 EU Strategy to Reduce Methane Emissions
				2.1.2.6 A Renovation Wave for Europe
				2.1.2.7 EU Commission Recommendation on Energy Poverty
				2.1.2.8 EU Strategy to Harness the Potential of Offshore Renewable Energy for a Climate-Neutral Future
				2.1.2.9 Smart Mobility Strategy
				2.1.2.10 Updating the 2020 New Industrial Strategy: Building a Stronger Single Market for Europe´s Recovery
			2.1.3 The European Green Deal and the 17 SDGs
		2.2 Alignment Between Energy-Related Policies and the 17 SDGs
		2.3 A Machine Learning Method to Evaluate the Connection Between Policy Documents and the 17 SDGs
			2.3.1 Information Retrieval
		2.4 Results 1
			2.4.1 Deep Learning
		2.5 Results 2
		2.6 Discussion on the Results
		2.7 Conclusions-Ideas for Further Research
		References
	Chapter 3: Single-Valued Neutrosophic CRITIC-Based ARAS Method for the Assessment of Sustainable Circular Supplier Selection
		3.1 Introduction
		3.2 Literature Review
		3.3 Preliminaries
		3.4 SVN-CRITIC-ARAS Method
		3.5 Case Study: Evaluation of ``Sustainable Circular Supplier Selection (SCSS)´´
			3.5.1 Comparison and Discussion
				3.5.1.1 SVN-TOPSIS Model
				3.5.1.2 SVN-VIKOR Method
			3.5.2 Managerial Implication
		3.6 Conclusions
		References
Part II: Climate Change
	Chapter 4: Linguistic-Based MCDM Approach for Climate Change Risk Evaluation Methodology
		4.1 Introduction
		4.2 Theoretical Backgrounds
			4.2.1 Climate Change and Supply Chain Management in Academic Literature
			4.2.2 Climate Change and Supply Chain Management in Industrial Reports
			4.2.3 Climate Change and Supply Chain Risks
		4.3 Suggested Methodology
		4.4 Case Study
			4.4.1 Results and Analysis
		4.5 Managerial Implications
		4.6 Concluding Remarks
		References
	Chapter 5: Creating a Net-Zero Carbon Emission Scenario Using OSeMOSYS for the Power Sector of Turkey
		5.1 Introduction
		5.2 Literature Review
		5.3 Methodology
		5.4 Proposed Model
		5.5 Conclusion
		References
	Chapter 6: Prediction of Downward Surface Solar Radiation Using Particle Swarm Optimization and Neural Networks
		6.1 Introduction
		6.2 Literature Review
		6.3 Methodology and Data
			6.3.1 Methodology
			6.3.2 Data
		6.4 Results and Discussion
		6.5 Conclusion
		References
Part III: Sustainability Energy Markets
	Chapter 7: Electricity Demand Prediction: Case of Turkey
		7.1 Introduction
		7.2 Methods
			7.2.1 Artificial Neural Networks (ANNs)
			7.2.2 Multiple Linear Regression (MLR)
			7.2.3 Autoregressive Integrated Moving Average Exogenous Variable Models (ARIMAX)
		7.3 Case of Turkey
		7.4 Comparison of Prediction Methods
		7.5 Summary and Conclusion
		References
	Chapter 8: The Impact of the Wind Energy Power Forecast Accuracy on the Price of Electricity
		8.1 Introduction
			8.1.1 Literature Review
			8.1.2 Data
		8.2 Methodology
		8.3 Results and Discussion
		8.4 Conclusion
		References
	Chapter 9: The Power of Combination Models in Energy Demand Forecasting
		9.1 Introduction
		9.2 Methodology
			9.2.1 Model Selection
				9.2.1.1 Performance Metrics
				9.2.1.2 Hypothesis Testing
				9.2.1.3 Graphical Inspections
			9.2.2 Combining Time Series Forecasts
		9.3 Results and Discussion
		9.4 Conclusion
		References
Part IV: Energy Efficiency
	Chapter 10: Data-Driven State Classification for Energy Modeling of Machine Tools Using Power Signals and Part-Program Informa...
		10.1 Introduction and Contribution
		10.2 Energy Monitoring of Machine Tools and State Identification
			10.2.1 Related Literature on Machine Energy Models
			10.2.2 Challenges as in the Literature
		10.3 Data-Driven Approach for State Classification
			10.3.1 Data Pre-processing
			10.3.2 MLAs for State Classification
		10.4 Real Case Application
			10.4.1 Case Description
			10.4.2 Data Preparation and Pre-processing
			10.4.3 MLA Classification Performance
			10.4.4 Sensitivity Analysis
		10.5 Conclusive Remarks
		References
	Chapter 11: Energy Efficiency Optimization Application in Food Production Using IIOT Based Machine Learning
		11.1 Introduction
			11.1.1 Challenges in Production
			11.1.2 Need of Analytic in Manufacturing
			11.1.3 Type of Analytic
		11.2 Literature Review
		11.3 Methodology
		11.4 Problem Statement
		11.5 Industrial Case Study
			11.5.1 Overview
			11.5.2 Data Operations
				11.5.2.1 Linear Regression
				11.5.2.2 XGBoost
				11.5.2.3 Random Forest
			11.5.3 Ensemble Model
			11.5.4 Results
		11.6 Conclusion
		References
Part V: Smart Cities
	Chapter 12: Hype: A Data-Driven Tool for Smart City Profile (SCP) Discrimination
		12.1 Introduction
		12.2 Methodology
			12.2.1 Modeling Smart City Profiles (SCP)
			12.2.2 Computing Smart City Profiles (SCP)
				12.2.2.1 Simplicial Complexes to Study Connectivity
				12.2.2.2 Hype, a Tool to Compute Simplicial Complexes
		12.3 Application
		12.4 Conclusion
		References
	Chapter 13: An Integrated Hesitant Fuzzy Linguistic MCDM Methods to Assess Smart City Solutions
		13.1 Introduction
		13.2 The Research Subject: Smart City Concept and Smart City Solutions
			13.2.1 Smart City Concept
			13.2.2 The Proposed Smart City Model and Solutions
		13.3 The Proposed Integrated Research Methodology
		13.4 Application
		13.5 Conclusion
		References
	Chapter 14: Presence of Renewable Resources in a Smart City for Supplying Clean and Sustainable Energy
		14.1 Introduction
		14.2 Renewable Resources and Sustainable Development
			14.2.1 Energy Security
			14.2.2 Socioeconomic Development
			14.2.3 Energy Access
			14.2.4 Climate Change
		14.3 Smart Energy System
			14.3.1 Smart Power Grid
			14.3.2 Smart Thermal Grid
			14.3.3 Smart Gas Grid
		14.4 Smart Energy Network for Smart City
			14.4.1 Solar Energy
				14.4.1.1 Solar Water Heating
				14.4.1.2 Seasonal Thermal Energy Storage (STES) System
			14.4.2 Wind
			14.4.3 Geothermal Energy
		14.5 Conclusion
		References
	Chapter 15: Syrian Household Energy Consumption Behavior Analysis in Turkey: Bayesian Belief Network
		15.1 Introduction
		15.2 Literature Review
			15.2.1 Main Drivers Shaping Energy Consumption Behavior
			15.2.2 Studies Concerning Migrants
			15.2.3 Bayesian Belief Network Applications on the Energy Consumption
		15.3 Methods
			15.3.1 Survey on Migrated Households
			15.3.2 Bayesian Belief Network
		15.4 Results and Discussions
		15.5 Conclusions
		References
Part VI: Modelling the Sustainable Future
	Chapter 16: Informativeness in Twitter Textual Contents for Farmer-Centric Pest Monitoring
		16.1 Introduction
		16.2 Related Works
			16.2.1 Crowdsensing for Agriculture
			16.2.2 NLP for Twitter-Based Crowdsensing
		16.3 Use Cases and Methodology
			16.3.1 Data Collection
			16.3.2 Histogram by Mention of Keywords
			16.3.3 Topic Detection Based on Bag of Word Models
			16.3.4 Text Classification Based on Pre-trained Language Models
		16.4 Conclusion
		References
	Chapter 17: A Multi-criteria Decision-Making Model for Technology Selection in Renewable-Based Residential Microgrids
		17.1 Introduction
		17.2 Literature Review: Renewable Energy Technology Selection from Sustainability Perspective
		17.3 Methodology: AHP- and TOPSIS-Based Decision Support System for Technology Selection in Renewable-Based Residential Microg...
		17.4 Application: A Renewable-Based Residential Microgrid in Antalya, Turkey
		17.5 Analysis
		17.6 Conclusion
		References
	Chapter 18: Energy Management in Power-Split Hybrid Electric Vehicles Using Fuzzy Logic Controller
		18.1 Introduction
		18.2 Energy Management and Control Strategy in Power-Split HEV Configuration
		18.3 Fuzzy Controller Design for Energy Management
			18.3.1 Fuzzification of Inputs
			18.3.2 Fuzzy Inference System
			18.3.3 Defuzzification of Output
		18.4 Implementation of Fuzzy Controller in HEV Model Using AVL CRUISE
		18.5 Simulation Results and Discussion
			18.5.1 Eighty Percent Initial SOC Without Fuzzy Logic Controller (Case A)
			18.5.2 Eighty Percent Initial SOC with Fuzzy Logic Controller (Case B)
			18.5.3 Forty-Five Percent Initial SOC Without Fuzzy Logic Controller (Case C)
			18.5.4 Forty-Five Percent Initial SOC With Fuzzy Logic Controller (Case D)
		18.6 Conclusions
		References
Correction to: The Power of Combination Models in Energy Demand Forecasting