DocumentCode
3384065
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
fYear
2012
fDate
14-17 Oct. 2012
Firstpage
1
Lastpage
8
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Innovative Smart Grid Technologies (ISGT Europe), 2012 3rd IEEE PES International Conference and Exhibition on
Conference_Location
Berlin
ISSN
2165-4816
Print_ISBN
978-1-4673-2595-0
Electronic_ISBN
2165-4816
Type
conf
DOI
10.1109/ISGTEurope.2012.6465677
Filename
6465677
Link To Document