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Decision making using AI in energy and sustainabilitymethods and models for policy and practice /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Decision making using AI in energy and sustainabilityedited by Gulgun Kayakutlu, M. Ozgur Kayalica.
其他題名:	methods and models for policy and practice /
其他作者:	Kayakutlu, Gulgun.
出版者:	Cham :Springer International Publishing :2023.
面頁冊數:	xi, 312 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Renewable energy sourcesData processing.
電子資源:	https://doi.org/10.1007/978-3-031-38387-8
ISBN:	9783031383878$q(electronic bk.)

Decision making using AI in energy and sustainabilitymethods and models for policy and practice /
Decision making using AI in energy and sustainabilitymethods and models for policy and practice /[electronic resource] :edited by Gulgun Kayakutlu, M. Ozgur Kayalica. - Cham :Springer International Publishing :2023. - xi, 312 p. :ill., digital ;24 cm. - Applied innovation and technology management,2662-9410. - Applied innovation and technology management..

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.

ISBN: 9783031383878$q(electronic bk.)

Standard No.: 10.1007/978-3-031-38387-8doiSubjects--Topical Terms:

524598
Renewable energy sources
--Data processing.

LC Class. No.: TJ808 / .D43 2023

Dewey Class. No.: 333.794

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520 $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.
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