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This book summarizes the electricity load forecasting has gained substantial importance nowadays in the modern electrical power management systems with elements of smart greed technology. Power big data has the characteristics of a large number, high dimension and time series. At the same time, there are many forms of missing electric power data, some are missing dispersedly, and some are missing in succession. Therefore, combinations of prediction methods are receiving increasing attention. We performed exploratory data analysis, pre-processing, and train- test split before training the model. We used various metrics to test the advantages of the proposed model: mean absolute error, mean squared error, and root mean squared error.
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Machine Learning Based Power Utilization Prediction
Editore:
LAP LAMBERT Academic Publishing Mai 2023, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
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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 -This book summarizes the electricity load forecasting has gained substantial importance nowadays in the modern electrical power management systems with elements of smart greed technology. Power big data has the characteristics of a large number, high dimension and time series. At the same time, there are many forms of missing electric power data, some are missing dispersedly, and some are missing in succession. Therefore, combinations of prediction methods are receiving increasing attention. We performed exploratory data analysis, pre-processing, and train- test split before training the model. We used various metrics to test the advantages of the proposed model: mean absolute error, mean squared error, and root mean squared error. 64 pp. Englisch. Codice articolo 9786206164500
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Machine Learning Based Power Utilization Prediction
Editore:
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book summarizes the electricity load forecasting has gained substantial importance nowadays in the modern electrical power management systems with elements of smart greed technology. Power big data has the characteristics of a large number, high dimension and time series. At the same time, there are many forms of missing electric power data, some are missing dispersedly, and some are missing in succession. Therefore, combinations of prediction methods are receiving increasing attention. We performed exploratory data analysis, pre-processing, and train- test split before training the model. We used various metrics to test the advantages of the proposed model: mean absolute error, mean squared error, and root mean squared error. Codice articolo 9786206164500
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Machine Learning Based Power Utilization Prediction
Editore:
LAP Lambert Academic Publishing, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
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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. This book summarizes the electricity load forecasting has gained substantial importance nowadays in the modern electrical power management systems with elements of smart greed technology. Power big data has the characteristics of a large number, high dimens. Codice articolo 887410489
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Machine Learning Based Power Utilization Prediction
Editore:
LAP LAMBERT Academic Publishing Mai 2023, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
Nuovo
Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
Valutazione del venditore 5 su 5 stelle
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book summarizes the electricity load forecasting has gained substantial importance nowadays in the modern electrical power management systems with elements of smart greed technology. Power big data has the characteristics of a large number, high dimension and time series. At the same time, there are many forms of missing electric power data, some are missing dispersedly, and some are missing in succession. Therefore, combinations of prediction methods are receiving increasing attention. We performed exploratory data analysis, pre-processing, and train- test split before training the model. We used various metrics to test the advantages of the proposed model: mean absolute error, mean squared error, and root mean squared error.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch. Codice articolo 9786206164500
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