Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32476
Title: A framework for electricity price spike analysis with advanced data mining methods
Authors: Zhao, JH
Dong, ZY
Li, X
Wong, KP
Keywords: Classification
Data mining
Electricity market
Electricity price forecast
Feature selection
Price spike reduction
Issue Date: 2007
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2007, v. 22, no. 1, p. 376-385 How to cite?
Journal: IEEE transactions on power systems 
Abstract: There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining-based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are first described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms - support vector machine and probability classifier - are chosen to be the spike occurrence predictors and are discussed in detail. Realistic market data are used to test the proposed model with promising results.
URI: http://hdl.handle.net/10397/32476
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2006.889139
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