Applications of UNI Variate Time Series Models and Neural Networks
Forecasting of Electricity Load in Andhra Pradesh using Neural Network
(Sprache: Englisch)
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...
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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.
Autoren-Porträt von Ramakrishna Ravi
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 in Abroad and has published about 12 research papers to his credit from peer-reviewed journals and conferences.
Bibliographische Angaben
- Autor: Ramakrishna Ravi
- 2020, 140 Seiten, Masse: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 620056857X
- ISBN-13: 9786200568571
Sprache:
Englisch
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