Intelligent Energy Demand Forecasting
By: by Wei-Chiang Hong
Contributors: SpringerLink (Online service)
Series: Lecture Notes in Energy -- 10
Publisher: Springer London; Imprint: Springer
Published: 2013
Place of publication: London
Language: eng
Format: [electronic resource] /
Number of pages: 189
ISBN-13: 9781447149682
Subjects: Energy Technology; Simulation and Modeling; Power (Mechanics); Engineering economy; Computer simulation; Energy Economics; Electrical engineering; Economics and Management Energy Policy; Power resources; Force and energy
Description: As industrial, commercial, and residential demands increase and with the rise of privatization and deregulation of the electric energy industry around the world, it is necessary to improve the performance of electric operational management. Intelligent Energy Demand Forecasting offers approaches and methods to calculate optimal electric energy allocation to reach equilibrium of the supply and demand. Evolutionary algorithms and intelligent analytical tools to improve energy demand forecasting accuracy are explored and explained in relation to existing methods. To provide clearer picture of how these hybridized evolutionary algorithms and intelligent analytical tools are processed, Intelligent Energy Demand Forecasting emphasizes on improving the drawbacks of existing algorithms.  Written for researchers, postgraduates, and lecturers, Intelligent Energy Demand Forecasting helps to develop the skills and methods to provide more accurate energy demand forecasting by employing novel hybridized evolutionary algorithms and intelligent analytical tools.
Table of contents: title: 1.Introduction; title: 2.Modeling for Energy Demand Forecasting; title: 3.Evolutionary Algorithms in SVR’s Parameters Determination; title: 4.Chaos/Cloud Theories to Avoid Trapping into Local Optimum; title: 5.Recurrent/Seasonal Mechanism to Improve the Accurate Level of Forecasting.