Items related to Machine Learning Based Power Utilization Prediction
Synopsis
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
Published by
LAP LAMBERT Academic Publishing Mai 2023, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786206164500
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Machine Learning Based Power Utilization Prediction
Published by
LAP LAMBERT Academic Publishing, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786206164500
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Machine Learning Based Power Utilization Prediction
Published by
LAP Lambert Academic Publishing, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
New
Softcover
Seller: moluna, Greven, Germany
Seller rating 5 out of 5 stars
Condition: 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. Seller Inventory # 887410489
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Machine Learning Based Power Utilization Prediction
Published by
LAP LAMBERT Academic Publishing Mai 2023, 2023
ISBN 10: 6206164500
ISBN 13: 9786206164500
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: 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. Seller Inventory # 9786206164500