Artificial Neural Network Model for Hourly Peak Load Forecast

V. Ramesh Kumar and Pradipkumar Dixit
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V. Ramesh Kumar: Department of Electrical and Electronics Engineering, School of Engineering and Technology, Jain University, Jain Global Campus, Bengaluru - 562 112, Karnataka, India,
Pradipkumar Dixit: Department of Electrical and Electronics Engineering, M. S. Ramaiah Institute of Technology, Bengaluru - 560 054, Karnataka, India. *

International Journal of Energy Economics and Policy, 2018, vol. 8, issue 5, 155-160

Abstract: Artificial Neural Network model for short-term demand forecast of hourly peak load is proposed in this paper. For learning of the ANN model Levenberg-Marquardt algorithm is adopted because of its ability to handle the large number of non-linear load data. The training of network is done by using hourly peak load data of preceding five years from the period of forecast and the temperature data. The validation of the developed ANN model is tested with historical load data of BESCOM (Bangalore Electricity Supply Company Limited) power system. The comparison of conventional methods and ANN model with respect to percentage error is evaluated, from the results it has been found that the proposed ANN model with optimal number of hidden layer neurons gives accurate predictions.

Keywords: Artificial Neural Network; Normalization; Forecasting (search for similar items in EconPapers)
JEL-codes: C8 Q47 (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eco:journ2:2018-05-20

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