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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Dr. 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.
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Taschenbuch. Zustand: Neu. Applications of UNI Variate Time Series Models and Neural Networks | Forecasting of Electricity Load in Andhra Pradesh using Neural Network | Ramakrishna Ravi | Taschenbuch | Englisch | 2020 | LAP LAMBERT Academic Publishing | EAN 9786200568571 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Artikel-Nr. 118420477
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