Decision making using AI in energy and sustainability: methods and models for policy and practice
Gespeichert in:
Weitere beteiligte Personen: | , |
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Format: | Buch |
Sprache: | Englisch |
Veröffentlicht: |
Cham
Springer International Publishing
[2023]
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Schriftenreihe: | Applied Innovation and Technology Management
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Schlagwörter: | |
Abstract: | 1. Climate change – Can AI help understanding and more effective facing of various interrelated impacts?- 2. A methodology for linking the Energy-related Policies of the European Green Deal to the 17 SDGs using Machine Learning -- 3. Single-valued neutrosophic CRITIC-based ARAS method for the assessment of sustainable circular supplier selection -- 4. Linguistic-Based MCDM Approach for Climate Change Risk Evaluation Methodology -- 5. Creating a Net-Zero Carbon Emission Scenario Using OSeMOSYS for the Power Sector of Turkey -- 6. Prediction of Downward Surface Solar Radiation Using Particle Swarm Optimization and Neural Networks -- 7. Electricity Demand Prediction: Case of Turkey -- 8. The Impact Of The Wind Energy Power Forecast Accuracy On The Price Of Electricity -- 9. The Power of Combination Models in Energy Demand Forecasting -- 10. Data-driven state classification for energy modeling of machine tools using power signals and part-program information -- 11. Energy Efficiency Optimization Application in Food Production using IIOT based Machine Learning -- 12. Hype: a data-driven tool for smart city profile (SCP) discrimination -- 13. An Integrated Hesitant Fuzzy Linguistic MCDM Methods to Assess Smart City Solutions -- 14. Presence of Renewable Resources in a Smart City for Supplying Clean and Sustainable Energy -- 15. Syrian Household Energy Consumption Behavior Analysis In Turkey: Bayesian Belief Network -- 16. Informativeness in Twitter Textual Contents for Farmer-centric Pest Monitoring -- 17. A Multi-Criteria Decision-Making Model for Technology Selection in Renewable-Based Residential Microgrids -- 18. Energy Management in Power-Split Hybrid Electric Vehicles Using Fuzzy Logic Controller. Artificial intelligence (AI) has a huge impact on science and technology, including energy, where access to resources has been a source of geopolitical conflicts. AI can predict the demand and supply of renewable energy, optimize efficiency in energy systems, and improve the management of natural energy resources, among other things. This book explores the use of AI tools for improving the management of energy systems and providing sustainability with smart cities, smart facilities, smart buildings, smart transportation, and smart houses. Featuring research from International Federation for Information Processing's (IFIP) "AI in Energy and Sustainability" working group, this book provides new models and algorithms for AI applications in energy and sustainability fields. Any short-term, mid-term and long-term forecasting, optimization models, trend foresights and prescriptions based on scenarios are studied in the energy world and the smart systems for sustainability. The contents of this book are valuable for energy researchers, academics, scholars, practitioners and policy makers. |
Beschreibung: | Literaturangaben |
Umfang: | xi, 312 Seiten Illustrationen 24 cm |
ISBN: | 9783031383861 |
Internformat
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520 | 3 | |a 1. Climate change – Can AI help understanding and more effective facing of various interrelated impacts?- 2. A methodology for linking the Energy-related Policies of the European Green Deal to the 17 SDGs using Machine Learning -- 3. Single-valued neutrosophic CRITIC-based ARAS method for the assessment of sustainable circular supplier selection -- 4. Linguistic-Based MCDM Approach for Climate Change Risk Evaluation Methodology -- 5. Creating a Net-Zero Carbon Emission Scenario Using OSeMOSYS for the Power Sector of Turkey -- 6. Prediction of Downward Surface Solar Radiation Using Particle Swarm Optimization and Neural Networks -- 7. Electricity Demand Prediction: Case of Turkey -- 8. The Impact Of The Wind Energy Power Forecast Accuracy On The Price Of Electricity -- 9. The Power of Combination Models in Energy Demand Forecasting -- 10. Data-driven state classification for energy modeling of machine tools using power signals and part-program information -- 11. Energy Efficiency Optimization Application in Food Production using IIOT based Machine Learning -- 12. Hype: a data-driven tool for smart city profile (SCP) discrimination -- 13. An Integrated Hesitant Fuzzy Linguistic MCDM Methods to Assess Smart City Solutions -- 14. Presence of Renewable Resources in a Smart City for Supplying Clean and Sustainable Energy -- 15. Syrian Household Energy Consumption Behavior Analysis In Turkey: Bayesian Belief Network -- 16. Informativeness in Twitter Textual Contents for Farmer-centric Pest Monitoring -- 17. A Multi-Criteria Decision-Making Model for Technology Selection in Renewable-Based Residential Microgrids -- 18. Energy Management in Power-Split Hybrid Electric Vehicles Using Fuzzy Logic Controller. | |
520 | 3 | |a Artificial intelligence (AI) has a huge impact on science and technology, including energy, where access to resources has been a source of geopolitical conflicts. AI can predict the demand and supply of renewable energy, optimize efficiency in energy systems, and improve the management of natural energy resources, among other things. This book explores the use of AI tools for improving the management of energy systems and providing sustainability with smart cities, smart facilities, smart buildings, smart transportation, and smart houses. Featuring research from International Federation for Information Processing's (IFIP) "AI in Energy and Sustainability" working group, this book provides new models and algorithms for AI applications in energy and sustainability fields. Any short-term, mid-term and long-term forecasting, optimization models, trend foresights and prescriptions based on scenarios are studied in the energy world and the smart systems for sustainability. The contents of this book are valuable for energy researchers, academics, scholars, practitioners and policy makers. | |
653 | 0 | |a Business information services. | |
653 | 0 | |a Artificial intelligence. | |
653 | 0 | |a Sustainability. | |
653 | 0 | |a Energy policy. | |
653 | 0 | |a Energy and state. | |
653 | 0 | |a European Green Deal | |
653 | 0 | |a The UN SDGs | |
653 | 0 | |a Sustainable circular supplier selection | |
653 | 0 | |a Solar Radiation | |
653 | 0 | |a Electricity Demand Prediction | |
653 | 0 | |a Smart city profile | |
653 | 0 | |a Residential microgrid Management | |
653 | 0 | |a Power-Split Hybrid Electric Vehicles | |
653 | 0 | |a Wind Energy Power Forecast | |
653 | 0 | |a Smart cities and clean energy | |
653 | 0 | |a Climate change risk evaluation | |
655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
700 | 1 | |a Kayakutlu, Gülgün |0 (DE-588)113705851X |4 edt | |
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776 | 0 | |z 9783031383861 | |
776 | 0 | |z 9783031383885 | |
776 | 0 | |z 9783031383892 | |
776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 9783031383878 |
943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035524558 |
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spelling | Decision making using AI in energy and sustainability methods and models for policy and practice Gülgün Kayakutlu, M. Özgür Kayalica Editiors Decision making using Artificial Intelligence in energy and sustainability Cham Springer International Publishing [2023] xi, 312 Seiten Illustrationen 24 cm txt rdacontent n rdamedia nc rdacarrier Applied Innovation and Technology Management Literaturangaben 1. Climate change – Can AI help understanding and more effective facing of various interrelated impacts?- 2. A methodology for linking the Energy-related Policies of the European Green Deal to the 17 SDGs using Machine Learning -- 3. Single-valued neutrosophic CRITIC-based ARAS method for the assessment of sustainable circular supplier selection -- 4. Linguistic-Based MCDM Approach for Climate Change Risk Evaluation Methodology -- 5. Creating a Net-Zero Carbon Emission Scenario Using OSeMOSYS for the Power Sector of Turkey -- 6. Prediction of Downward Surface Solar Radiation Using Particle Swarm Optimization and Neural Networks -- 7. Electricity Demand Prediction: Case of Turkey -- 8. The Impact Of The Wind Energy Power Forecast Accuracy On The Price Of Electricity -- 9. The Power of Combination Models in Energy Demand Forecasting -- 10. Data-driven state classification for energy modeling of machine tools using power signals and part-program information -- 11. Energy Efficiency Optimization Application in Food Production using IIOT based Machine Learning -- 12. Hype: a data-driven tool for smart city profile (SCP) discrimination -- 13. An Integrated Hesitant Fuzzy Linguistic MCDM Methods to Assess Smart City Solutions -- 14. Presence of Renewable Resources in a Smart City for Supplying Clean and Sustainable Energy -- 15. Syrian Household Energy Consumption Behavior Analysis In Turkey: Bayesian Belief Network -- 16. Informativeness in Twitter Textual Contents for Farmer-centric Pest Monitoring -- 17. A Multi-Criteria Decision-Making Model for Technology Selection in Renewable-Based Residential Microgrids -- 18. Energy Management in Power-Split Hybrid Electric Vehicles Using Fuzzy Logic Controller. Artificial intelligence (AI) has a huge impact on science and technology, including energy, where access to resources has been a source of geopolitical conflicts. AI can predict the demand and supply of renewable energy, optimize efficiency in energy systems, and improve the management of natural energy resources, among other things. This book explores the use of AI tools for improving the management of energy systems and providing sustainability with smart cities, smart facilities, smart buildings, smart transportation, and smart houses. Featuring research from International Federation for Information Processing's (IFIP) "AI in Energy and Sustainability" working group, this book provides new models and algorithms for AI applications in energy and sustainability fields. Any short-term, mid-term and long-term forecasting, optimization models, trend foresights and prescriptions based on scenarios are studied in the energy world and the smart systems for sustainability. The contents of this book are valuable for energy researchers, academics, scholars, practitioners and policy makers. Business information services. Artificial intelligence. Sustainability. Energy policy. Energy and state. European Green Deal The UN SDGs Sustainable circular supplier selection Solar Radiation Electricity Demand Prediction Smart city profile Residential microgrid Management Power-Split Hybrid Electric Vehicles Wind Energy Power Forecast Smart cities and clean energy Climate change risk evaluation (DE-588)4143413-4 Aufsatzsammlung gnd-content Kayakutlu, Gülgün (DE-588)113705851X edt Kayalıca, M. Özgür (DE-588)137142358 edt 9783031383861 9783031383885 9783031383892 Erscheint auch als Online-Ausgabe 9783031383878 |
spellingShingle | Decision making using AI in energy and sustainability methods and models for policy and practice |
subject_GND | (DE-588)4143413-4 |
title | Decision making using AI in energy and sustainability methods and models for policy and practice |
title_alt | Decision making using Artificial Intelligence in energy and sustainability |
title_auth | Decision making using AI in energy and sustainability methods and models for policy and practice |
title_exact_search | Decision making using AI in energy and sustainability methods and models for policy and practice |
title_full | Decision making using AI in energy and sustainability methods and models for policy and practice Gülgün Kayakutlu, M. Özgür Kayalica Editiors |
title_fullStr | Decision making using AI in energy and sustainability methods and models for policy and practice Gülgün Kayakutlu, M. Özgür Kayalica Editiors |
title_full_unstemmed | Decision making using AI in energy and sustainability methods and models for policy and practice Gülgün Kayakutlu, M. Özgür Kayalica Editiors |
title_short | Decision making using AI in energy and sustainability |
title_sort | decision making using ai in energy and sustainability methods and models for policy and practice |
title_sub | methods and models for policy and practice |
topic_facet | Aufsatzsammlung |
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