Полное описание
> Hong, W. -Ch. Intelligent energy demand forecasting / W.-Ch. Hong. - London [etc.] : Springer, 2013. - on-line. - (Lecture notes in energy ; vol. 10). - URL: http://link.springer.com/book/10.1007/978-1-4471-4968-2. - Загл. с экрана. - ISBN 978-1-4471-4968-2. - Текст : электронный.
Содержание: >
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.
ГРНТИ | УДК | |
44.29.39 | 621.31.03-047.72 |
Рубрики:
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
Кл.слова (ненормированные): ИНТЕЛЛЕКТУАЛЬНЫЕ АНАЛИТИЧЕСКИЕ ИНСТРУМЕНТЫ -- ПРОГНОЗИРОВАНИЕ -- ЭВОЛЮЦИОННЫЕ АЛГОРИТМЫ -- ЭЛЕКТРОПОТРЕБЛЕНИЕ
Аннотация: 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.
http://link.springer.com/book/10.1007/978-1-4471-4968-2
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Государственная публичная научно-техническая библиотека России : 123298, г. Москва, ул. 3-я Хорошевская, д. 17 (Шифр в БД-источнике (KATBW): ?-349440376)>
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