TY  - BOOK
AU  - Hong,Wei-Chiang
ED  - SpringerLink (Online service)
TI  - Intelligent Energy Demand Forecasting
T2  - Lecture Notes in Energy,
SN  - 9781447149682
AV  - HD9502-9502.5 
U1  - 333.79 23
PY  - 2013///
CY  - London
PB  - Springer London, Imprint: Springer
KW  - Energy
KW  - Energy policy
KW  - Energy and state
KW  - Computer simulation
KW  - Electric power production
KW  - Energy industries
KW  - Energy Policy, Economics and Management
KW  - Energy Technology
KW  - Simulation and Modeling
KW  - Energy Economics
N1  - 1.Introduction -- 2.Modeling for Energy Demand Forecasting -- 3.Evolutionary Algorithms in SVR’s Parameters Determination -- 4.Chaos/Cloud Theories to Avoid Trapping into Local Optimum -- 5.Recurrent/Seasonal Mechanism to Improve the Accurate Level of Forecasting
N2  - 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
UR  - http://dx.doi.org/10.1007/978-1-4471-4968-2
ER  -