In this letter, we present a novel Gaussian Process Learning-based Probabilistic Optimal Power Flow (GP-POPF) for solving POPF under renewable and load uncertainties of arbitrary distribution. The proposed method relies on non-parametric Bayesian inference-based uncertainty propagation approach, called (GP). We also suggest new type sensitivity Subspace-wise Sensitivity, using observations the ...

This paper presents a new approach to robust Gaussian process regression, creating non-parametric Bayesian regression estimate outliers. Most existing approaches replace an outlier-prone likelihood with non-Gaussian induced from heavy tail distribution, such as the Laplace distribution and Student-t distribution. However, use of would incur need for computationally expensive approximate computa...

The Gaussian process (GP) regression can be severely biased when the data are contaminated by outliers. This paper presents a new robust GP algorithm that iteratively trims most extreme points. While retains attractive properties of standard as nonparametric and flexible method, it greatly improve model accuracy for even in presence or abundant It is also easier to implement compared with previ...

This paper proposes a novel approach of Gaussian process observation set compression based on squared difference measure. It is used to discard observations speed up prediction while retaining the information encoded in full observations. Furthermore, this compares regression performance compressed its uncompressed version and randomly downsampled for standard two-dimensional test function. The...