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A fuzzy-neural approach to electricity load and spot-price forecasting in a deregulated electricity market

Iyer, V., Fung, C.C. and Gedeon, T. (2003) A fuzzy-neural approach to electricity load and spot-price forecasting in a deregulated electricity market. In: TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, 15-17 October 2007, Bangalore, India pp. 1479-1482.

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Link to Published Version: http://dx.doi.org/10.1109/TENCON.2003.1273164
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Abstract

Accurate short term load forecasting is crucial to the efficient and economic operation of modern electrical power systems. With the recent effort by many governments in the development of open and deregulated power markets, research in forecasting methods is getting renewed attention, Although long term and short term electric load forecasting has been of interest to the practicing engineers and researchers for many years, spot-price prediction is a relatively new research area. This paper examines the use of a neural-fuzzy inference method for the prediction of 24 hourly load and spot price for the next day. Publicly available data of the electricity market of the state of New South Wales, Australia is used in a case study.

Publication Type: Conference Paper
Murdoch Affiliation: School of Information Technology
Publisher: IEEE
Copyright: (c) 2003 IEEE.
Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
URI: http://researchrepository.murdoch.edu.au/id/eprint/618
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