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Stacked Denoising Auto-Encoder for Short-Term Load Forecasting: Deep Learning with Stacked Denoising Auto-Encoder Algorithm - Brossura
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The Stacked Denoising Auto-Encoder (SDAE) is adopted firstly for short-term load forecasting using four factors. The daily average loads act as the baseline in final forecasting tasks. In this research, the Denoising Auto-Encoder (DAE) is pre-trained. In the symmetric DAE, there are three layers: the input layer, the hidden layer, and the output layer where the hidden layer is the symmetric axis. The input layer and the hidden layer construct the encoding part while the hidden layer and the output layer construct the decoding part. After that, all DAEs are stacked together for fine-tuning. In addition, in the encoding part of each DAE, the weight values and hidden layer values are combined with the original input layer values to establish an SDAE network for load forecasting.
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Stacked Denoising Auto-Encoder for Short-Term Load Forecasting
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LAP LAMBERT Academic Publishing Aug 2019, 2019
ISBN 10: 6200278571
ISBN 13: 9786200278579
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The Stacked Denoising Auto-Encoder (SDAE) is adopted firstly for short-term load forecasting using four factors. The daily average loads act as the baseline in final forecasting tasks. In this research, the Denoising Auto-Encoder (DAE) is pre-trained. In the symmetric DAE, there are three layers: the input layer, the hidden layer, and the output layer where the hidden layer is the symmetric axis. The input layer and the hidden layer construct the encoding part while the hidden layer and the output layer construct the decoding part. After that, all DAEs are stacked together for fine-tuning. In addition, in the encoding part of each DAE, the weight values and hidden layer values are combined with the original input layer values to establish an SDAE network for load forecasting. 52 pp. Englisch. Codice articolo 9786200278579
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Stacked Denoising Auto-Encoder for Short-Term Load Forecasting
Editore:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200278571
ISBN 13: 9786200278579
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Zheng PeijunName: Zheng Peijun. Academic title: Assistant.Graduate School: North China Electric Power University Yangzhong Intelligent Electric Research Center. Research direction: Electric load forecasting in Microgrids.The Stac. Codice articolo 385887066
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Stacked Denoising Auto-Encoder for Short-Term Load Forecasting: Deep Learning with Stacked Denoising Auto-Encoder Algorithm
Editore:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200278571
ISBN 13: 9786200278579
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Da: Revaluation Books, Exeter, Regno Unito
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Paperback. Condizione: Brand New. 52 pages. 8.66x5.91x0.12 inches. In Stock. Codice articolo zk6200278571
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Stacked Denoising Auto-Encoder for Short-Term Load Forecasting
Editore:
LAP LAMBERT Academic Publishing Aug 2019, 2019
ISBN 10: 6200278571
ISBN 13: 9786200278579
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The Stacked Denoising Auto-Encoder (SDAE) is adopted firstly for short-term load forecasting using four factors. The daily average loads act as the baseline in final forecasting tasks. In this research, the Denoising Auto-Encoder (DAE) is pre-trained. In the symmetric DAE, there are three layers: the input layer, the hidden layer, and the output layer where the hidden layer is the symmetric axis. The input layer and the hidden layer construct the encoding part while the hidden layer and the output layer construct the decoding part. After that, all DAEs are stacked together for fine-tuning. In addition, in the encoding part of each DAE, the weight values and hidden layer values are combined with the original input layer values to establish an SDAE network for load forecasting.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch. Codice articolo 9786200278579
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Stacked Denoising Auto-Encoder for Short-Term Load Forecasting : Deep Learning with Stacked Denoising Auto-Encoder Algorithm
Editore:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200278571
ISBN 13: 9786200278579
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The Stacked Denoising Auto-Encoder (SDAE) is adopted firstly for short-term load forecasting using four factors. The daily average loads act as the baseline in final forecasting tasks. In this research, the Denoising Auto-Encoder (DAE) is pre-trained. In the symmetric DAE, there are three layers: the input layer, the hidden layer, and the output layer where the hidden layer is the symmetric axis. The input layer and the hidden layer construct the encoding part while the hidden layer and the output layer construct the decoding part. After that, all DAEs are stacked together for fine-tuning. In addition, in the encoding part of each DAE, the weight values and hidden layer values are combined with the original input layer values to establish an SDAE network for load forecasting. Codice articolo 9786200278579
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Stacked Denoising Auto-Encoder for Short-Term Load Forecasting | Deep Learning with Stacked Denoising Auto-Encoder Algorithm
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200278571
ISBN 13: 9786200278579
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Stacked Denoising Auto-Encoder for Short-Term Load Forecasting | Deep Learning with Stacked Denoising Auto-Encoder Algorithm | Peijun Zheng | Taschenbuch | 52 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200278579 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 117388546
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