Title :
Electricity price forecasting considering residual demand
Author :
Motamedi, Ali ; Geidel, C. ; Zareipour, Hamidreza ; Rosehart, W.D.
Author_Institution :
Alberta Electr. Syst. Operator (AESO), Calgary, AB, Canada
Abstract :
In this paper, short-term electricity price forecasting considering residual electricity demand is investigated. Residual, or net, demand is determined by subtracting any unpredictable generation from the system load. Focusing on wind energy as the main hard-to-predict source of electricity, we first examine the dependency of short-term electricity prices and wind power using data association mining algorithms. Second, we investigate the impact of including net demand in short-term electricity price forecasting, and we propose a new electricity price forecasting model. Data from the Alberta and the Nordic electricity markets are used to conduct studies and evaluate the forecasting results.
Keywords :
data mining; load forecasting; power engineering computing; power markets; pricing; sensor fusion; wind power plants; Alberta electricity markets; Nordic electricity markets; data association mining algorithms; residual electricity demand; short-term electricity price forecasting model; system load; wind energy; wind power; Data mining; Electricity; Electricity supply industry; Forecasting; Pragmatics; Predictive models; Wind power generation; Price forecasting; residual demand; smart grid; wind power;
Conference_Titel :
Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on
Conference_Location :
Berlin
Print_ISBN :
978-1-4673-2595-0
Electronic_ISBN :
2165-4816
DOI :
10.1109/ISGTEurope.2012.6465677