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Applications of UNI Variate Time Series Models and Neural Networks: Forecasting of Electricity Load in Andhra Pradesh using Neural Network - Couverture souple
Synopsis
Forecasting of daily and monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks determination be compared with those obtained by Box-Jenkins method. The performances were compared based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The Final conclusion of this book is Feed-Forward neural networks models are better and superior than Box-Jenkins models.
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Applications of UNI Variate Time Series Models and Neural Networks
Edité par
LAP LAMBERT Academic Publishing Mrz 2020, 2020
ISBN 10 : 620056857X
ISBN 13 : 9786200568571
Neuf
Taschenbuch
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Forecasting of daily and monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks determination be compared with those obtained by Box-Jenkins method. The performances were compared based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The Final conclusion of this book is Feed-Forward neural networks models are better and superior than Box-Jenkins models. 140 pp. Englisch. N° de réf. du vendeur 9786200568571
Quantité disponible : 2 disponible(s)
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Applications of UNI Variate Time Series Models and Neural Networks
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 620056857X
ISBN 13 : 9786200568571
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ravi RamakrishnaDr. Ravi.Ramakrishna is a Professor in Statistics VJIT (A), Hyderabad. He has completed Ph.D. (Statistics) from Osmania University. He has more than 14 years of experience in VJIT,Hyderabad and 4 years of experience. N° de réf. du vendeur 385895108
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Applications of UNI Variate Time Series Models and Neural Networks
Edité par
LAP LAMBERT Academic Publishing Mär 2020, 2020
ISBN 10 : 620056857X
ISBN 13 : 9786200568571
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -Forecasting of daily and monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks determination be compared with those obtained by Box-Jenkins method. The performances were compared based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The Final conclusion of this book is Feed-Forward neural networks models are better and superior than Box-Jenkins models.Books on Demand GmbH, Überseering 33, 22297 Hamburg 140 pp. Englisch. N° de réf. du vendeur 9786200568571
Quantité disponible : 2 disponible(s)
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Applications of UNI Variate Time Series Models and Neural Networks : Forecasting of Electricity Load in Andhra Pradesh using Neural Network
Edité par
LAP LAMBERT Academic Publishing, 2020
ISBN 10 : 620056857X
ISBN 13 : 9786200568571
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Forecasting of daily and monthly electricity load using Box-Jenkins methodology and feed forward neural networks is discussed. This study investigates application of neural networks models and the results of neural networks determination be compared with those obtained by Box-Jenkins method. The performances were compared based on three measures: mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The Final conclusion of this book is Feed-Forward neural networks models are better and superior than Box-Jenkins models. N° de réf. du vendeur 9786200568571
Quantité disponible : 1 disponible(s)