Wind power probabilistic forecast in the Reproducing Kernel Hilbert Space

Abstract

Abstract-Wind power probabilistic forecast is a key input in decision-making problems under risk, such as stochastic unit commitment, operating reserve setting and electricity market bidding. While the majority of the probabilistic forecasting methods are based on quantile regression, the associated limitations call for new approaches. This paper described a new quantile regression model based on the Reproducing Kernel Hilbert Space (RKHS) framework. In particular, two versions of the model, off-line and on-line, were implemented and tested for a real wind farm. Results showed the superiority of the on-line approach in terms of performance, robustness and computational cost. Additionally, it was observed that, in the presence of correlated data, the optimal on-line learning may cause unreliable modelling. Potential solutions to this effect are also described and implemented in the paper.

A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data

“…Gallego-Castillo et al (2016) provided a quantile relapse model dependent on the recreating piece Hilbert space (RKHS) system to predict the wind power probabilistic. Furthermore, they implemented two types of models (online and offline) for a real wind farm [25]. Xiao et al (2017) employed an electrical power system prediction model using a wavelet neural network (WNN) model and an improved cuckoo search algorithm.…”

On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power

“…The algorithm is implemented from an on-line learning perspective. While the main advantage of this approach is to account for smooth variations in the underlying dynamics of the modelled process, other advantages as compared with the off-line approach were analysed in [25].…”