Wind power forecasting based on refined LSTAR-GARCH model
Year: 2017
Type: article
Abstract: Wind Power forecasting (WPF) plays a crucial role in the secure and economic operation of power systems. To improve precision of the WPF model, the latent information in volatility characteristics of the wind power time series needs to be further explored. In this study, with a novel smooth transition autoregressive (STAR) type model, the multiple regime switching effect on the volatility of wind power time series is investigated. First, the traditional logistic STAR are provided, and the separation of different regime in the volatility of time series is discussed preliminarily. Second, a refined STAR structure with the novel smooth transition function is presented to depict the double asymmetric effect. By combining the refined LSTAR structure and the generalised autoregressive conditional heteroskedasticity (GARCH) framework, a prospective RLSTAR-GARCH WPF model is proposed. Moreover, considering the fat-tail effect in the volatility of wind power time series, RLSTAR-GARCH models with fat-tail distribution are proposed for generalisation. Case studies on a practical sample for WPF validate the feasibility and effectiveness of this method. With the smooth transition structure, the behaviours of RLSTAR-GARCH type model near the threshold are highlighted. Forecasting performance comparison shows that the RLSTAR model is promising for WPF.
Authors: Hao Chen, Ruomei Li, Yurong Wang, Chao Xu
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2768239772