Feature selection for probabilistic load forecasting via sparse penalized quantile regression

Feature selection for probabilistic load forecasting via sparse penalized quantile regression

Probabilistic load forecasting (PLF) is able to present the uncertainty information of the future loads. It is the basis of stochastic power system planning and operation. Recent works on PLF mainly focus on how to develop and combine forecasting models, while the feature selection issue has not bee...

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Bibliographic Details
Main Authors: Yi Wang, Dahua Gan, Ning Zhang, Le Xie, Chongqing Kang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:Journal of Modern Power Systems and Clean Energy
Subjects:
Probabilistic load forecasting
Feature selection
Alternating direction method of multipliers (ADMM)
Quantile regression
<tex>$L_{1}$</tex>-norm penalty
Online Access:https://ieeexplore.ieee.org/document/8952836/

Internet

https://ieeexplore.ieee.org/document/8952836/

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