• ВХОД

    •  

    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
    44.29.39621.31.03-047.72

    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


    Держатели документа:
    Государственная публичная научно-техническая библиотека России : 123298, г. Москва, ул. 3-я Хорошевская, д. 17 (Шифр в БД-источнике (KATBW): ?-349440376)

    Шифр в сводном ЭК: 026d649015155c81ef206a4fb0aee12b



    Заказ фрагмента документа ₽

    Просмотр издания