A Rotating Short-Term Wind Speed Forecasting Model

• Activity Number: 138
• Type: Contributed
• Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Xinxin Zhu*+ and Marc Genton and Yingzhong Gu and Le Xie
• Companies: Texas A&M University and Texas A&M University and Texas A&M University and Texas A&M University
• Keywords: space-time statistical models ; short-term wind speed forecasting ; power system dispatch ; wind energy

Abstract

In order to support large scale integration of wind power, state-of-the-art wind speed forecasting methods should provide accurate and adequate information to enable efficient scheduling of wind power in electric energy systems. In this article, we propose a new space-time wind forecasting model, which generalizes and improves upon a so-called regime-switching space-time (RSTD) model (Gneiting et al. 2006) by allowing the forecast regimes to vary with the dominant wind direction and with the seasons. This new model outperforms the persistence and autoregressive models and achieves better or similar accuracy than the RSTD and trigonometric diurnal direction (TDD) model (Hering and Genton 2010) in two-hour ahead forecasting problems. Further analyses show that the potential performance of the new model exceeds all aforementioned models in forecasting accuracy. Moreover, we incorporate the space-time wind forecasts into power system economic dispatch models through simulations to demonstrate the potential economic benefits in the system-wide generation cost savings and in the ancillary service cost savings with improved wind forecasts.