Electronic Resource

Advanced Methods of Power Load Forecasting

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This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.

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Availability

No copy data

Detail Information

Series Title

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Call Number

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Publisher

Basel, Switzerland : MDPI - Multidisciplinary Digital Publishing Institute., 2022

Collation

128hlm; ill., lamp.,

Language

English

ISBN/ISSN

9783036542188, 9783036542171

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Physics
Research and information: general

Specific Detail Info

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Statement of Responsibility

García-Díaz, J. Carlos (editor) Trull, Óscar (editor)

Other Information

Cataloger

ida

Source

https://directory.doabooks.org/handle/20.500.12854/84505

Validator

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Digital Object Identifier (DOI)

10.3390/books978-3-0365-4217-1

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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