CHAOTIC LOAD SERIES FORECASTING BASED ON MPMR
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 ε 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 will influence the MPMR-based system performance. In experiments, cross validation was used to select the two parameters.
Electrical Load Short-term Forecasting Minimax Probability Regression Chaos Theory
QUAN-HUA CHENG ZUN-XIONG LIU
School of Information Technology, Jiangxi University of Finance & Economics, Nanchang 330013, China School of Electronics and Electric Engineering, East China Jiaotong University, Nanchang 330013, Chi
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
2868-2871
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）