Applications of UNI Variate Time Series Models and Neural Networks

Ramakrishna Ravi

ISBN 10: 620056857X / ISBN 13: 9786200568571

Published by LAP LAMBERT Academic Publishing Mär 2020, 2020

About this Item

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.

Seller Inventory # 9786200568571

Bibliographic Details

• Title: Applications of UNI Variate Time Series Models and Neural Networks
• Publisher: LAP LAMBERT Academic Publishing Mär 2020
• Publication Date: 2020
• Language: English
• ISBN 10: 620056857X
• ISBN 13: 9786200568571
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 9D millimeters 220 millimeters width by 150 millimeters height by 9 millimeters depth
• Weight: 227 grams

About this title

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.