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Intelligent Energy Demand 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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Springer London : Imprint: Springer,
2013.
|
| Edición: | 1st ed. 2013. |
| Colección: | Lecture Notes in Energy,
10 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-1-4471-4968-2 | ||
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| 020 | |a 9781447149682 |9 978-1-4471-4968-2 | ||
| 024 | 7 | |a 10.1007/978-1-4471-4968-2 |2 doi | |
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| 100 | 1 | |a Hong, Wei-Chiang. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Intelligent Energy Demand Forecasting |h [electronic resource] / |c by Wei-Chiang Hong. |
| 250 | |a 1st ed. 2013. | ||
| 264 | 1 | |a London : |b Springer London : |b Imprint: Springer, |c 2013. | |
| 300 | |a XIII, 189 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Lecture Notes in Energy, |x 2195-1292 ; |v 10 | |
| 505 | 0 | |a 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. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Energy policy. | |
| 650 | 0 | |a Energy and state. | |
| 650 | 0 | |a Electric power production. | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 1 | 4 | |a Energy Policy, Economics and Management. |
| 650 | 2 | 4 | |a Electrical Power Engineering. |
| 650 | 2 | 4 | |a Mechanical Power Engineering. |
| 650 | 2 | 4 | |a Computer Modelling. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781447149699 |
| 776 | 0 | 8 | |i Printed edition: |z 9781447159308 |
| 776 | 0 | 8 | |i Printed edition: |z 9781447149675 |
| 830 | 0 | |a Lecture Notes in Energy, |x 2195-1292 ; |v 10 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-1-4471-4968-2 |z Texto Completo |
| 912 | |a ZDB-2-ENE | ||
| 912 | |a ZDB-2-SXEN | ||
| 950 | |a Energy (SpringerNature-40367) | ||
| 950 | |a Energy (R0) (SpringerNature-43717) | ||