Hybrid Intelligent Technologies in Energy Demand Forecasting
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Beschreibung
This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies.
It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory.
The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.
This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies.
It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory.
The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.
Describes the most advanced and accurate energy demand forecasting models Demonstrates how cutting-edge hybrid intelligent technologies can be combined with traditional models Includes a wealth of examples and illustrations to demonstrate the effectiveness of modern demand forecasting models
Autor*in
Wei-Chiang Hong
Themen in »Hybrid Intelligent Technologies in Energy Demand Forecasting«
Support Vector Regression Energy Demand Forecasting Meta-Heuristic Algorithms Chaotic Mapping Mechanism Quantum Computing Mechanism Recurrent Neural Network Mechanism Phase Space Reconstruction Recurrence Plot Theory