Title :
Application of linear lazy learning approach to short-term load forecasting
Author :
Ramezani, M. ; Gharaveisi, A.A. ; Rashidinejad, M. ; Rafiei, S.M.R. ; Barakati, S.M.
Author_Institution :
Dept. of Electr. Eng., Shahid Bahonar Univ. of Kerman, Kerman, Iran
Abstract :
Many plans on power systems strongly depend on short-term load forecasting. In this paper a novel method based on linear lazy learning approach is proposed for short-term electric load forecasting. The proposed method is successfully verified through PJM market forecasting. The model is trained by the data available for four years and the next two years data is used for validation. The results prove the ability and high-precision of the proposed approach.
Keywords :
learning (artificial intelligence); load forecasting; power engineering computing; power markets; power system planning; PJM market forecasting; linear lazy learning approach; power system planning; short-term electric load forecasting; Economic forecasting; Energy management; Error analysis; Least squares approximation; Load forecasting; Power generation; Power system management; Power system modeling; Power system planning; Predictive models; Lazy Learning (LL); STLF; linear Lazy Learning (LLL);
Conference_Titel :
Power Symposium, 2008. NAPS '08. 40th North American
Conference_Location :
Calgary, AB
Print_ISBN :
978-1-4244-4283-6
DOI :
10.1109/NAPS.2008.5307388
