Generative Probabilistic Wind Speed Forecasting: A Variational Recurrent Autoencoder Based Method

Abstract

In this paper, a novel framework for probabilistic wind speed forecasting (PWSF) based on variational recurrent autoencoders (VRAEs) via a generative perspective is proposed. Compared with a traditional optimization objective maximizing the conditional likelihood of the target wind speed directly, a novel optimization objective maximizing the likelihood of the complete wind speed sequence is proposed to better model the temporal relationship within the complete wind speed sequence. As directly maximizing the proposed objective is intractable, we show that it can be alternatively achieved via the help of the VRAE learning principle. The framework of the proposed VRAE based PWSF is composed of two phases, training a VRAE with maximizing the variational lower bound of the likelihood of the complete wind speed sequence and forecasting the target wind speed from the generative perspective through the approximate posterior learned by the VRAE. Computational results demonstrate that the proposed method outperforms other benchmarking deterministic and probabilistic forecasting models in terms of the negative form of continuous ranked probability score (CRPS*). Compared with other benchmarking models for probabilistic forecasting, the proposed method achieves better sharpness and overall quality of prediction intervals (PIs) as well as a comparable reliability. Results verify advantages of the proposed VARE based PWSF method.