Title :
Day periodical classification for wide area day ahead short-term load forecast
Author :
Fang Yuan Xu ; Loi Lei Lai
Author_Institution :
State Grid Energy Res. Inst., Beijing, China
Abstract :
Short-Term forecast technique is widely popular for accurate forecast in all sorts of future operation planning. In general future load is recognized as a non-linear mapping result from several previous step loads. This paper introduces a new ANN-based day ahead load forecast model for Wide Area in which loads are mapped from load pattern in previous day, rather than in previous steps load. With day periodical classification by k-means clustering, this new model achieves an excellent accuracy.
Keywords :
load forecasting; neural nets; power engineering computing; power system planning; ANN-based wide area day ahead load forecast model; day periodical classification; future operation planning; k-means clustering; nonlinear mapping; Artificial neural networks; Load forecasting; Load modeling; Mathematical model; Meteorology; Predictive models; Training; ANN; STLF; daily; day ahead; k means clustering;
Conference_Titel :
Power and Energy Society General Meeting, 2012 IEEE
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-2727-5
Electronic_ISBN :
1944-9925
DOI :
10.1109/PESGM.2012.6345133
