Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17120
Title: CMAC-based short-term electricity price forecasting
Authors: Zhou, H
Chen, J
Wu, H
Ho, SL 
Issue Date: 2003
Source: Sixth International Conference on Advances in Power System Control, Operation and Management - Proceedings, 2003, v. 1, p. 348-353 How to cite?
Abstract: Electricity price forecasting is, naturally, the basis of decision-making in electricity markets. This paper proposes the day-ahead short-term electricity price forecasting models using a Cerebella Model Articulation Controller (CMAC) neural network, and then constructs the respective models at different trading intervals. The data of California electricity market is employed to predict the short-term electricity price using the proposed CMAC and a BP (back-propagation) neural network. The performance comparison shows that the proposed CMAC neural network can work more steadily and speedily in short-term electricity price forecasting when compared with a BP network.
Description: Sixth International Conference on Advances in Power System Control, Operation and Management - Proceedings, Hong Kong, 11-14 November 2003
URI: http://hdl.handle.net/10397/17120
ISBN: 0863413285
9780863413285
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