The Application of Sparse Partial Least Squares Regression in Electricity Consumption of Yunnan Province

DOI

10.2991/cset-16.2016.70How to use a DOI?

Keywords

Partial least-squares regression, sparse partial least-squares regression, the electricity demand of Yunnan province, cross-validation

Abstract

It's extremely important to screen key variables from high-dimesional electricity data that contains many predic- tors and presents multi-collinearity. In this paper, sparse partial least-squares regression(SPLS) is employed to investigate the electricity consumption from Yunnan province of China. SPLS can automatically select important variables and simultaneously eliminate the uninformative variables. The root mean square errors(RMSE) is used to evaluate the prediction performance and the results show that SPLS is competitive with ordinary least squares (OLS) and partial least squares regression (PLS). In addition, several predictors such as GDP of Yunnan are chosen as key factors with SPLS algorithm.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shuting Peng
AU  - Lin Dai
AU  - Tingting Guo
PY  - 2016/08
DA  - 2016/08
TI  - The Application of Sparse Partial Least Squares Regression in Electricity Consumption of Yunnan Province
BT  - Proceedings of the 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SP  - 299
EP  - 303
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.70
DO  - 10.2991/cset-16.2016.70
ID  - Peng2016/08
ER  -