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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Dr M. Aravind Kumar uzyskä stopie¿ B. Tech w ECE, stopie¿ M.Tech w projektowaniu systemów VLSI w JNTUH i doktorat na Uniwersytecie GITAM w Visakhapatnam. Ma 15-letnie do¿wiadczenie w nauczaniu. Jest do¿ywotnim cz¿onkiem FIE, ISTE, IETE, SCIEI, UACEE i IAENG. Opublikowä 35 artyku¿ów naukowych w recenzowanych czasopismach i na konferencjach.
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786206164500
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Zustand: 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. Bestandsnummer des Verkäufers 887410489
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Taschenbuch. Zustand: Neu. 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. Bestandsnummer des Verkäufers 9786206164500
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786206164500
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