Title :
Forecasting system imbalance volumes in competitive electricity markets
Author :
Garcia, M.P. ; Kirschen, D.S.
Author_Institution :
Univ. of Manchester Inst. of Sci. & Technol., 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; load forecasting; neural nets; power markets; power system analysis computing; time series; England market; New Electricity Trading Arrangement; Wales market; data mining; electricity market; neural network; power system forecasting; time series; Data mining; Economic forecasting; Electricity supply industry; Industrial power systems; Load forecasting; Multidimensional systems; Neural networks; Power markets; Power systems; Uncertainty;
Conference_Titel :
Power Systems Conference and Exposition, 2004. IEEE PES
Print_ISBN :
0-7803-8718-X
DOI :
10.1109/PSCE.2004.1397617
