The Application of Sparse Partial Least Squares Regression in Electricity Consumption of Yunnan Province
Shuting Peng, Lin Dai, Tingting Guo
Available Online August 2016.
- https://doi.org/10.2991/cset-16.2016.70How to use a DOI?
- Partial least-squares regression, sparse partial least-squares regression, the electricity demand of Yunnan province, cross-validation
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/cset-16.2016.70 ID - Peng2016/08 ER -