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BL
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| Record Number
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864948
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| Title & Author
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Energy management -- collective and computational intelligence with theory and applications /\ Cengiz Kahraman, Gülgün Kayakutlu, editors.
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| Publication Statement
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Cham, Switzerland :: Springer,, 2018.
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| Series Statement
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Studies in systems, decision and control ;; volume 149
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| Page. NO
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1 online resource
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| ISBN
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3319756907
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: 9783319756905
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3319756893
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9783319756899
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| Contents
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Intro; Preface; Contents; About the Editors; Introduction; 1 Complexity in Energy Systems; Abstract; 1.1 Introduction; 1.2 Complexity Concepts; 1.3 Complexity in the Energy Markets; 1.3.1 Energy Resources; 1.3.2 Energy Distribution; 1.3.3 Socio-ecological approaches; 1.4 Response to Complexity: Computational and Collective Intelligence; 1.5 Conclusion; References; 2 Fuzzy Sets Applications in Complex Energy Systems: A Literature Review; Abstract; 2.1 Introduction; 2.2 Fuzzy Sets Theory; 2.2.1 Ordinary Fuzzy Sets and Their Extensions; 2.2.2 Interval-Valued Fuzzy Sets; 2.2.3 Type-n Fuzzy Sets
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2.2.4 Intuitionistic Fuzzy Sets2.2.5 Fuzzy Multisets; 2.2.6 Nonstationary Fuzzy Sets; 2.2.7 Hesitant Fuzzy Sets; 2.2.8 Neutrosophic Theory; 2.2.9 Pythagorean Fuzzy Sets-Type 2 Intuitionistic Fuzzy Sets; 2.3 Complex Energy Systems; 2.4 Literature Review: Complex Energy Systems and Fuzzy Sets Applications; 2.4.1 Bioenergy and Fuzzy Sets; 2.4.2 Wave Energy and Fuzzy Sets; 2.4.3 Photovoltaic Systems and Fuzzy Sets; 2.4.4 Hydrogen Energy and Fuzzy Sets; 2.4.5 Nuclear Energy and Fuzzy Sets; 2.4.6 Wind-Thermal Energy and Fuzzy Sets; 2.5 Conclusion; References; Forecasting
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3 Forecasting Super-Efficient Dryers Adoption in the Pacific NorthwestAbstract; 3.1 Introduction; 3.2 Literature Review; 3.2.1 Technology Forecasting; 3.2.2 Energy Efficiency; 3.2.3 Super Efficient Dryers; 3.2.3.1 Super Efficient Dryer Initiative (SEDI); 3.2.4 Other Emerging Types of Dryers; 3.2.4.1 Microwave Dryers; 3.2.4.2 Solar Clothes Dryer; 3.2.5 NEEA; 3.2.5.1 Midstream and Upstream Incentives; 3.2.5.2 Downstream Incentives; 3.3 Methodology; 3.4 Analysis; 3.5 Conclusions and Recommendations; 3.5.1 For NEEA; 3.5.2 General; 3.6 Future Work; References
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4 Fuzzy Forecasting Methods for Energy PlanningAbstract; 4.1 Introduction; 4.2 Literature Review; 4.3 Fuzzy Forecasting Methods; 4.3.1 Fuzzy Time Series; 4.3.2 Fuzzy Regression; 4.3.3 Fuzzy Inference Systems; 4.3.4 ANFIS; 4.3.5 Hwang, Chen, Lee's Fuzzy Time Series Method; 4.4 A Numerical Application; 4.5 Conclusion; Acknowledgements; References; 5 Smart Storage Scheduling and Forecasting for Peak Reduction on Low-Voltage Feeders; Abstract; 5.1 Introduction; 5.2 Forecasting Methods; 5.2.1 Data; 5.2.2 Methods; 5.2.2.1 A Simple Seasonal Method; 5.2.2.2 Random Forest Regression
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5.2.2.3 Support Vector Regression5.2.2.4 Benchmark Methods; 5.2.3 Analysis of Forecasts; 5.2.4 Discussion; 5.3 Application of Forecasts in Energy Storage Control; 5.3.1 Set-Point Control; 5.3.2 Fixed Day-Ahead Schedule; 5.3.3 Model Predictive Control; 5.3.4 Results; 5.3.5 Discussion; Acknowledgements; References; Economic Analysis; 6 Modeling and Economic Evaluation of PV Net-Metering and Self-consumption Schemes; Abstract; 6.1 Introduction; 6.2 Machine Learning Application; 6.2.1 PV Data Modeling; 6.2.2 PV Generation Profiles Per Installation; 6.2.3 PV Generation Profiles Per Cluster
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| Abstract
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This book presents a selection of recently developed collective and computational intelligence techniques, which it subsequently applies to energy management problems ranging from performance analysis to economic analysis, and from strategic analysis to operational analysis, with didactic numerical examples. As a form of intelligence emerging from the collaboration and competition of individuals, collective and computational intelligence addresses new methodological, theoretical, and practical aspects of complex energy management problems. The book offers an excellent reference guide for practitioners, researchers, lecturers and postgraduate students pursuing research on intelligence in energy management. The contributing authors are recognized researchers in the energy research field.
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| Subject
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Artificial intelligence.
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Energy conservation-- Data processing.
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Energy conservation-- Management.
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Energy conservation-- Mathematical models.
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Artificial intelligence.
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Artificial intelligence.
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Energy conservation-- Data processing.
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Energy conservation-- Management.
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Energy conservation-- Mathematical models.
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Energy technology engineering.
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Power generation distribution.
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TECHNOLOGY ENGINEERING-- Mechanical.
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| Dewey Classification
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621.042
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| LC Classification
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TJ163.3
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| Added Entry
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Kahraman, Cengiz
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Kayakutlu, Gülgün
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