DocumentCode :
3215143
Title :
Load forecasting via manifold regularization
Author :
Zhang, Lin ; Dai, Guang ; Zhai, Guixiang ; Cao, Yijia ; Liu, Zhaoyan
Author_Institution :
Northwest China Grid Co. Ltd., Xi´´an
fYear :
2009
fDate :
15-18 March 2009
Firstpage :
1
Lastpage :
6
Abstract :
The ability to accurately forecast the load plays an important role in electric power system planning and operating. In this paper, a novel approach was proposed for the electricity load forecasting by applying the manifold regularization learning methodology. Unlike traditional methods for load forecasting, the prediction method based on manifold regularization allows us to effectively exploit the geometric manifold structure of electricity load data in a semi-supervised learning setting. The effectiveness of the proposed approach is illustrated through an application to actual load data from the northwest China region.
Keywords :
geometry; learning (artificial intelligence); load forecasting; power engineering computing; power system planning; electric power system planning; electricity load data; geometric manifold structure; load forecasting; manifold regularization learning methodology; northwest China region; semi-supervised learning setting; Kernel; Load forecasting; Machine learning; Manifolds; Neural networks; Power system planning; Power system reliability; Prediction methods; Risk management; Semisupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power Systems Conference and Exposition, 2009. PSCE '09. IEEE/PES
Conference_Location :
Seattle, WA
Print_ISBN :
978-1-4244-3810-5
Electronic_ISBN :
978-1-4244-3811-2
Type :
conf
DOI :
10.1109/PSCE.2009.4840016
Filename :
4840016
Link To Document :
بازگشت