A Regime-Switching Spatio-temporal GARCH Method for Short-Term Wind Forecasting
- Resource Type
- Conference
- Authors
- Zhang, Wenqi; Feng, Cong; Hodge, Bri-Mathias
- Source
- 2022 IEEE Power & Energy Society General Meeting (PESGM) Power & Energy Society General Meeting (PESGM), 2022 IEEE. :1-6 Jul, 2022
- Subject
- Engineering Profession
Power, Energy and Industry Applications
Wind energy
Wind speed
Computational modeling
Wind farms
Predictive models
Benchmark testing
Wind power generation
Regime-switching
wind forecasting
spatio-temporal model
stGARCH
- Language
- ISSN
- 1944-9933
The growth of wind energy poses challenges to the integration of wind energy into the power grid. Within a wind farm, the conditions of local wind exhibit sizeable variations in very short term period and temporal wind speed patterns vary from turbine to turbine. Hence, short-term wind forecasting has been adopted to assist power system operations. In this work, we propose a wind plant-level short term wind speed and power forecasting methodology considering turbine contributions. The proposed model utilizes spatio-temporal dependencies and nonstationarity to accommodate the characteristics of wind farm data by using a novel regime-switching spatiotemporal generalized autoregressive conditional heteroscedasticity (RS-stGARCH) model. Case studies based on 2 years of data from a wind farm shows that the proposed RS-stGARCH method outperforms benchmark models by up to 21.10% for wind speed forecasting and up to 58.62% for the wind power forecasting.