Title :
Identification and modification of improper load data used in short-term load forecasting
Author :
Ansarimehr, P. ; Barghinia, S. ; Mirsepassi, Z. ; Habibi, H.
Author_Institution :
NRI, Tehran
Abstract :
Short term load forecasting (STLF) plays an important role for the power system operational planners and also most of the participants in the nowadays power markets. Data used for STLF is mostly historical hourly load and weather parameters. Hourly load data is subject to errors made by instruments and/or operators and also unwanted or unplanned changes in the power system. In this paper, an approach to identification and modification of improper load data used in STLF is proposed. The results for Bakhtar regional electric company (Bakhtar REC), a part of Iran national power system (INPS), shows that the idea of modifying improper input load data used for STLF can improve greatly the performance of the STLF itself.
Keywords :
load forecasting; power markets; Bakhtar regional electric company; absolute normalized residual; improper load data identification; power market; power system operational planner; short-term load forecasting; Artificial neural networks; Indium phosphide; Instruments; Load forecasting; Load modeling; Power markets; Power system planning; Power systems; Predictive models; Weather forecasting; Absolute Normalized Residual; Bad Data Identification; Improper Load Data; Short Term Load Forecast;
Conference_Titel :
Power Tech, 2005 IEEE Russia
Conference_Location :
St. Petersburg
Print_ISBN :
978-5-93208-034-4
Electronic_ISBN :
978-5-93208-034-4
DOI :
10.1109/PTC.2005.4524554
