Title :
Chaotic Load Series Forecasting Based on MPMR
Author :
Cheng, Quan-hua ; Liu, Zun-xiong
Author_Institution :
Sch. of Inf. Technol., Jiangxi Univ. of Finance & Econ., Nanchang
Abstract :
In this paper, minimax probability machine regression (MPMR) is proposed for chaotic load time series global prediction. In MPMR, regression function maximizes the minimum probability that future predication will be within an epsiv to the true regression function. After the theory of MPMR explored, and the chaotic property of the load series from a certain power system verified, one-day ahead predictions for 24 hours points next day were done with MPMR. The results demonstrated that MPMP had satisfactory prediction efficiency. Kernel function shape parameter and regression tube value influences the MPMR-based system performance. In experiments, cross validation was used to select the two parameters
Keywords :
chaos; load forecasting; minimax techniques; probability; regression analysis; time series; chaotic load time series forecasting; electrical load; kernel function shape parameter; minimax probability machine regression; power system; short-term forecasting; Chaos; Cybernetics; Economic forecasting; Finance; Information technology; Kernel; Load forecasting; Machine learning; Minimax techniques; Power generation economics; Power system modeling; Predictive models; Chaos Theory; Electrical Load; Minimax Probability Regression; Short-term Forecasting;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.259071
