Isbn: 9786206164500 - machine learning based power utilization prediction (4 risultati)

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  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Mai 2023, 2023

    6206164500 / 9786206164500

    • Brossura
    • Print on Demand

    Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

    Venditore con 5 stelle
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    Condizione: Nuovo

    EUR 43,90

    EUR 23,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 2 disponibili

    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.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing, 2023

    6206164500 / 9786206164500

    • Brossura
    • Print on Demand

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Condizione: Nuovo

    EUR 46,83

    EUR 35,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibile

    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.…

  • Lingua: Inglese

    Editore: LAP Lambert Academic Publishing, 2023

    6206164500 / 9786206164500

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    Da: moluna, Greven, Germaniamoluna

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    Condizione: Nuovo

    EUR 37,23

    EUR 48,99 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: Più di 20 disponibili

    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.…

  • Lingua: Inglese

    Editore: LAP LAMBERT Academic Publishing Mai 2023, 2023

    6206164500 / 9786206164500

    • Brossura
    • Print on Demand

    Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

    Venditore con 5 stelle
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    Condizione: Nuovo

    EUR 43,90

    EUR 60,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 1 disponibile

    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.…