02664oam 2200445 450 991043777660332120190911103511.01-4471-4968-810.1007/978-1-4471-4968-2(OCoLC)834096863(MiFhGG)GVRL6XNC(EXLCZ)99267000000053021720130219d2013 uy 0engurun|---uuuuatxtccrIntelligent energy demand forecasting /Wei-Chiang Hong1st ed. 2013.London :Springer,2013.1 online resource (xiii, 189 pages) illustrations (some color)Lecture Notes in Energy,2195-1284 ;10"ISSN: 2195-1284."1-4471-4967-X 1-4471-5930-6 Includes bibliographical references.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.Lecture notes in energy ;10.Power resourcesForecastingPower resourcesForecasting.333.7913Hong Wei-Chiangauthttp://id.loc.gov/vocabulary/relators/aut913784MiFhGGMiFhGGBOOK9910437776603321Intelligent Energy Demand Forecasting2047305UNINA