no code implementations • 31 Mar 2024 • Minghui Chen, Zichao Meng, Yanping Liu, Longbo Luo, Ye Guo, Kang Wang
In this paper, we introduce a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs while including missing data imputation.
no code implementations • 15 Aug 2023 • Zichao Meng, Ye Guo, Hongbin Sun
This paper studies an adaptive approach for probabilistic wind power forecasting (WPF) including offline and online learning procedures.
no code implementations • 27 Oct 2022 • Zichao Meng, Ye Guo, Wenjun Tang, Hongbin Sun
This paper proposes a nonparametric multivariate density forecast model based on deep learning.
no code implementations • 7 May 2021 • Zichao Meng, Ye Guo, Wenjun Tang, Hongbin Sun, Wenqi Huang
A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems.