Wind Speed Forecasting via Structured Output Learning

In the context of the wind energy management, the study of time series data by means of a predictability analysis can be very helpful. For example, accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the day-ahead electricity market. This paper examines the use of structured output learning, in order to model historical wind speed data and yield accurate forecasts of the wind speed on the day-ahead (24 h) horizon. The proposed method is based on a multi-resolution analysis of the historical data, which are represented at multiple scales in both space and time. Handling multi-resolution wind speed data allows us to leverage the knowledge hidden in both the spatial and temporal variability of the shared information, in order to identify spatio-temporal aided patterns that contribute to yield accurate wind speed forecasts. In an assessment, using benchmark data, we show that the multi-resolution structured output learning is able to determine more accurate forecasts than the state-of-the-art structured output models.