NDLI: A radial basis function neural network approach for multi-hour short term load-price forecasting with type of day parameter

Content Provider	IEEE Xplore Digital Library
Author	Singh, N.K. Tripathy, M. Singh, A.K.
Copyright Year	2011
Description	Author affiliation: Electrical Engineering Department, Indian Institute of Technology, Roorkee, India (Tripathy, M.) \|\| Electrical Engineering Dept., Motilal Nehru National Institute of Technology, Allahabad, India (Singh, N.K.; Singh, A.K.)
Abstract	In 1990s, after deregulation of Australian electricity market, electricity became a commodity that can be bought and sold. This led power industry to change their planning strategies. In this planning Short Term Load Forecasting (STLF) plays a vital role to provide unit commitment, economic generation scheduling etc. In this paper, RBF neural network (RBFNN) is applied as short term load as well as price forecaster. While modeling process, day-type (Sunday, Monday, etc.) is considered as an extra input to the neural network. The prediction performance of proposed RBFNN architecture is evaluated using Mean of Mean Absolute Percentage Error (MMAPE) between actual data and forecasted data of New South Wales (Australia). The results obtained are compared with the results gained from classical moving average (MA), Holt-Winters and Feed Forward Neural Network (FFNN) methods. It is, in general, observed that the RBFNN is more accurate and works better with inclusion of day type input parameters.
Starting Page	316
Ending Page	321
File Size	1383536
Page Count	6
File Format	PDF
ISBN	9781457700323
e-ISBN	9781457700354
DOI	10.1109/ICIINFS.2011.6038087
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-16
Publisher Place	Sri Lanka
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Deregulation Neurons Predictive models radial basis function neural network (RBFNN) feed forward neural network (FFNN) Forecasting Training Load forecasting Electricity moving average method electricity planning load forecasting Load modeling
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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