Decision Making Using AI in Energy and Sustainability : Methods and Models for Policy and Practice / / Gülgün Kayakutlu and M. Özgür Kayalica, editors

• Pubblicazione: Cham, Switzerland : , : Springer, Springer Nature Switzerland AG, , [2023]; ©2023
• Edizione: First edition.
• Descrizione fisica: 1 online resource (306 pages)
• Disciplina: 333.79
• Soggetto topico: Artificial intelligence - Environmental aspects; Artificial intelligence - Industrial applications; Power resources - Management - Data processing; Renewable energy sources - Data processing
• Persona (resp. second.): KayakutluGülgün; KayalicaM. Özgür
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- 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.
• Titolo autorizzato: Decision Making Using AI in Energy and Sustainability
• ISBN: 3-031-38387-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910751386403321
• Lo trovi qui: Univ. Federico II
• Serie: Applied language studies.