Sparse Variational Gaussian Process Based Day-Ahead Probabilistic Wind Power Forecasting

Abstract

In this paper, we present a probabilistic wind power forecasting (PWPF) model via quantification of epistemic uncertainty and aleatory uncertainty. Concretely, the epistemic uncertainty is described by the statistical characteristics of function space constituted by all wind power forecasting (WPF) mappings through Gaussian process (GP) frameworks. In particular, we adopt the sparse variational Gaussian process to address inference complexity and hyperparameters determination issues, which impede the performance of existing GP-based PWPF models. It introduces inducing variables and variational inference to minimize the difference between the approximated sparse GP model and the original GP, whereby a variational distribution is introduced to explicitly represent the inducing variables. All parameters are optimized via gradient descent optimization based on likelihood maximization. Experiments based on an open dataset demonstrate that the proposed model is comparable to state-of-the-art in terms of continuous ranked probability score, and robust to overfitting.