Title :
Forecasting system imbalance volumes in competitive electricity markets
Author :
Garcia, Maria P. ; Kirschen, Daniel S.
Author_Institution :
Dept. of Electr. Eng. & Electron., Univ. of Manchester, UK
Abstract :
Forecasting in power systems has been made considerably more complex by the introduction of competitive electricity markets. Furthermore, new variables need to be predicted by various market participants. This paper shows how a new methodology that combines classical and data mining techniques can be used to forecast the system imbalance volume, a key variable for the system operator in the market of England and Wales under the New Electricity Trading Arrangements (NETA).
Keywords :
data mining; power engineering computing; power markets; England market; Wakes market; competitive electricity markets; data mining techniques; forecasting system imbalance volumes; new electricity trading arrangements; power system forecasting; system operator; Data mining; Economic forecasting; Electricity supply industry; Industrial power systems; Load forecasting; Multidimensional systems; Neural networks; Power markets; Power systems; Uncertainty; Data mining; electricity markets; multidimensional forecasting; neural networks; time series;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2005.860924
