Mining Cluster based Models of Time Series for Wind Power Prediction

The growing integration of wind turbines into the power grid can only be balanced with precise forecasts of upcoming energy productions. This information plays as basis for operation and management strategies for a reliable and economical integration into the power grid. A precise forecast needs to overcome problems of variable energy production caused by fluctuating weather conditions. In this paper, we define a data mining approach, in order to process a past set of the wind power measurements of a wind turbine and extract a robust prediction model. We resort to a time series clustering algorithm, in order to extract a compact, informative representation of the time series of wind power measurements in the past set. We use cluster prototypes for predicting upcoming wind powers of the turbine. We illustrate a case study with real data collected from a wind turbine installed in the Apulia region.