TY - GEN
T1 - A general method for electricity market price spike analysis
AU - Zhao, JunHua
AU - Dong, Zhaoyang
AU - Li, Xue
AU - Wong, Kit Po
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - Many techniques have been employed to forecast electricity market prices, and have achieved good results. These techniques mostly focus on normal price forecasting, not on the forecasting of price spikes. Data mining techniques have been successfully applied to forecast the value of price spikes under the condition that a spike appears. However, an effective method of predicting the occurrence of the spikes is yet to be seen. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with spike value prediction techniques, the proposed approach can give a comprehensive price spike forecasting. In this paper, data pre-process techniques are described to find the attributes relevant to the spikes. Then a simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen and discussed in detail. Actual market data are used to test the proposed model, and promising results have been obtained. ©2005 IEEE.
AB - Many techniques have been employed to forecast electricity market prices, and have achieved good results. These techniques mostly focus on normal price forecasting, not on the forecasting of price spikes. Data mining techniques have been successfully applied to forecast the value of price spikes under the condition that a spike appears. However, an effective method of predicting the occurrence of the spikes is yet to be seen. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with spike value prediction techniques, the proposed approach can give a comprehensive price spike forecasting. In this paper, data pre-process techniques are described to find the attributes relevant to the spikes. Then a simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen and discussed in detail. Actual market data are used to test the proposed model, and promising results have been obtained. ©2005 IEEE.
UR - https://www.scopus.com/pages/publications/27144518111
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-27144518111&origin=recordpage
U2 - 10.1109/pes.2005.1489199
DO - 10.1109/pes.2005.1489199
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 078039156
VL - 1
T3 - 2005 IEEE Power Engineering Society General Meeting
SP - 286
EP - 293
BT - 2005 IEEE Power Engineering Society General Meeting
T2 - 2005 IEEE Power Engineering Society General Meeting
Y2 - 12 June 2005 through 16 June 2005
ER -