Intelligent Energy Demand Forecasting
Hong, Wei-Chiang.
Intelligent Energy Demand Forecasting [electronic resource] / by Wei-Chiang Hong. - XIII, 189 p. online resource. - Lecture Notes in Energy, 10 2195-1284 ; . - Lecture Notes in Energy, 10 .
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.
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.
9781447149682
10.1007/978-1-4471-4968-2 doi
Energy.
Energy policy.
Energy and state.
Computer simulation.
Electric power production.
Energy industries.
Energy.
Energy Policy, Economics and Management.
Energy Technology.
Simulation and Modeling.
Energy Economics.
HD9502-9502.5
333.79 338.926
Intelligent Energy Demand Forecasting [electronic resource] / by Wei-Chiang Hong. - XIII, 189 p. online resource. - Lecture Notes in Energy, 10 2195-1284 ; . - Lecture Notes in Energy, 10 .
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.
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.
9781447149682
10.1007/978-1-4471-4968-2 doi
Energy.
Energy policy.
Energy and state.
Computer simulation.
Electric power production.
Energy industries.
Energy.
Energy Policy, Economics and Management.
Energy Technology.
Simulation and Modeling.
Energy Economics.
HD9502-9502.5
333.79 338.926