Applications of UNI Variate Time Series Models and Neural Networks

Ramakrishna Ravi

ISBN 10: 620056857X / ISBN 13: 9786200568571

Verlag: LAP LAMBERT Academic Publishing Mrz 2020, 2020

Beschreibung

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.

Bestandsnummer des Verkäufers 9786200568571

Bibliografische Details

• Titel: Applications of UNI Variate Time Series Models and Neural Networks
• Verlag: LAP LAMBERT Academic Publishing Mrz 2020
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 620056857X
• ISBN-13: 9786200568571
• Einband: Taschenbuch
• Zustand: Neu
• Abmessungen: 220B x 150 H x 9 T Millimeter Breite: 220 mm, Höhe: 150 mm, Tiefe: 9 mm
• Gewicht: 227 Gramm

Über diesen Titel

Inhaltsangabe

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.