Title :
Historical load curve correction for short-term load forecasting
Author :
Yang, Jingfei ; Stenzel, Jürgen
Author_Institution :
Dept. of Electr. Eng. & Inf. Technol., Darmstadt Univ. of Technol.
fDate :
Nov. 29 2005-Dec. 2 2005
Abstract :
Short-term load forecasting (STLF) is a significant task for power system operation. The existence of bad data in historical load curve affects the precision of load forecasting result. This paper presents the second order difference method to detect the bad data, eliminate them and evaluate the real data. To decrease the effect of impulse load on the prediction result, weighted least square quadratic fitting is proposed to filter the curve. K-means clustering and support vector machine method are employed to forecast the future load. The proposed method is successfully applied to an actual power system
Keywords :
curve fitting; difference equations; least squares approximations; load forecasting; support vector machines; K-means clustering; bad data detection; bad data elimination; historical load curve correction; power system operation; second order difference method; short-term load forecasting; support vector machine method; weighted least square quadratic fitting; Curve fitting; Filtering; Filters; Information technology; Least squares methods; Load forecasting; Metals industry; Power systems; Steel; Support vector machines; impulsive load; load forecasting; second order difference bad data detection; support vector machine; virtual prediction; weighted least square quadratic fitting;
Conference_Titel :
Power Engineering Conference, 2005. IPEC 2005. The 7th International
Conference_Location :
Singapore
Print_ISBN :
981-05-5702-7
DOI :
10.1109/IPEC.2005.206875
