Local Partial Least Squares Multi-Step Model for Short-Term Load Forecasting

Zunxiong LIU; Xin XIE; Deyun ZHANG; Haiyuan LIU

doi:10.1093/ietfec/e89-a.10.2740

Local Partial Least Squares Multi-Step Model for Short-Term Load Forecasting

Zunxiong LIU, Xin XIE, Deyun ZHANG, Haiyuan LIU

Full Text Views

0

Cite this

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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E89-A No.10 pp.2740-2744

Publication Date: 2006/10/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e89-a.10.2740

Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)

Category: Modelling, Systems and Simulation

Cite this

Copy

Zunxiong LIU, Xin XIE, Deyun ZHANG, Haiyuan LIU, "Local Partial Least Squares Multi-Step Model for Short-Term Load Forecasting" in IEICE TRANSACTIONS on Fundamentals, vol. E89-A, no. 10, pp. 2740-2744, October 2006, doi: 10.1093/ietfec/e89-a.10.2740.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e89-a.10.2740/_p

Copy

@ARTICLE{e89-a_10_2740,
author={Zunxiong LIU, Xin XIE, Deyun ZHANG, Haiyuan LIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Local Partial Least Squares Multi-Step Model for Short-Term Load Forecasting},
year={2006},
volume={E89-A},
number={10},
pages={2740-2744},
abstract={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.},
keywords={},
doi={10.1093/ietfec/e89-a.10.2740},
ISSN={1745-1337},
month={October},}

Copy

TY - JOUR
TI - Local Partial Least Squares Multi-Step Model for Short-Term Load Forecasting
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2740
EP - 2744
AU - Zunxiong LIU
AU - Xin XIE
AU - Deyun ZHANG
AU - Haiyuan LIU
PY - 2006
DO - 10.1093/ietfec/e89-a.10.2740
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E89-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2006
AB - 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.
ER -