Summary
|
For Full-Text PDF, please login, if you are a member of IEICE, or go to Pay Per View on menu list, if you are a nonmember of IEICE. |
|
Local Partial Least Squares Multi-Step Model for Short-Term Load Forecasting Zunxiong LIU Xin XIE Deyun ZHANG Haiyuan LIU Publication IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences Vol.E89-A No.10 pp.2740-2744 Publication Date: 2006/10/01 Online ISSN: 1745-1337 DOI: 10.1093/ietfec/e89-a.10.2740 Print ISSN: 0916-8508 Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications) Category: Modelling, Systems and Simulation Keyword: load series, multi-step prediction, chaos theory, partial least squares, local model, Full Text: PDF(217.8KB)>>
Summary: The multi-step prediction model based on partial least squares (PLS) is established to predict short-term load series with high embedding dimension in this paper, which refrains from cumulative error with local single-step linear model, and can cope with the multi-collinearity in the reconstructed phase space. In the model, PLS is used to model the dynamic evolution between the phase points and the corresponding future points. With research on the PLS theory, the model algorithm is put forward. Finally, the actual load series are used to test this model, and the results show that the model plays well in chaotic time series prediction, even if the embedding dimension is selected a big value. | ||||||||||
| ||||||||||
