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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/17149
Title: A comparative study of BPNN, RBFNN and ELMAN neural network for short-term electric load forecasting: A case study of Delhi region
Authors: Singh N.K.
Singh A.K.
Tripathy M.
Arya K.V.
Kumar S.
Published in: Proceedings of 9th International Conference on Industrial and Information Systems, ICIIS 2014
Abstract: Constant tariff scheme produces a large and continuously-changing difference between electricity cost and price. Consequently, the concern of power system planning and economic generation becomes significant. To overcome this problem accurate load forecasting is a field of immense importance. Conventional methods, i.e., Moving Average (MA) and Holt-Winter (HW) methods are inappropriate to forecast in highly non-linear electrical environment, as existing in Delhi region. In this paper, electrical Load (L), Temperature (T), Relative Humidity (RH) and atmospheric Pressure (Pr) of New Delhi, India are analysed and used to develop the load forecasting model. This paper presents the results of an investigation on various Artificial Neural Networks (ANNs), i.e., Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN) and ELMAN Neural Network (ELMNN), together with specified conventional methods, due to non-linear mapping characteristics of electrical load. Day-Type (D) is additionally used as an input parameter to improve the forecasting accuracy. The investigation has shown that the ELMNN is more accurate than other ANN structures and conventional methods. © 2014 IEEE.
Citation: Proceedings of 9th International Conference on Industrial and Information Systems, ICIIS 2014, (2015)
URI: https://doi.org/10.1109/ICIINFS.2014.7036502
http://repository.iitr.ac.in/handle/123456789/17149
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Back propagation neural network
ELMAN neural network
Moving average method
Power system planning
Radial basis function neural network
Toad forecasting
ISBN: 9781479964994
Author Scopus IDs: 57214658977
57209046775
16205441100
Author Affiliations: Singh, N.K., Electrical Engineering Department, MNNIT Allahabad, Uttar Pradesh, 211004, India
Singh, A.K., Electrical Engineering Department, MNNIT Allahabad, Uttar Pradesh, 211004, India
Tripathy, M., Electrical Engineering Department, Indian Institu
Appears in Collections:Conference Publications [EE]

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