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220822 ||| eng |
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|a 9783036542188
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|a 9783036542171
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|a books978-3-0365-4217-1
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|a García-Díaz, J. Carlos
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|a Advanced Methods of Power Load Forecasting
|h Elektronische Ressource
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260 |
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|a Basel
|b MDPI - Multidisciplinary Digital Publishing Institute
|c 2022
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300 |
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|a 1 electronic resource (128 p.)
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|a machine learning
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|a forecast
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|a Artificial Neural Network
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|a short-term load forecasting
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|a prophet model
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|a deep learning
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|a irregular
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|a time series
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|a deep neural network
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|a short-term load forecast
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653 |
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|a LSTM
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|a encoder decoder
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653 |
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|a Prophet model
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|a multi-layer stacked
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|a power system
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|a load
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|a Holt-Winters model
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|a galvanizing
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|a Physics / bicssc
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|a bidirectional long short-term memory
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|a recurrent neural network
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|a CNN
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|a long-term forecasting
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|a peak load
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|a short-term electrical load forecasting
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|a statistical analysis
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|a parameters tuning
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|a demand
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|a online training
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|a DIMS
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|a Research and information: general / bicssc
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|a neural network
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|a attention
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|a multiple seasonality
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1 |
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|a Trull, Óscar
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|a García-Díaz, J. Carlos
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|a Trull, Óscar
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|a eng
|2 ISO 639-2
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|b DOAB
|a Directory of Open Access Books
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|a Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/
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|a 10.3390/books978-3-0365-4217-1
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856 |
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|u https://directory.doabooks.org/handle/20.500.12854/84505
|z DOAB: description of the publication
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|u https://www.mdpi.com/books/pdfview/book/5489
|7 0
|x Verlag
|3 Volltext
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|a 000
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|a 530
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|a 700
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|a 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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