Associating weather conditions with ramp events in wind power generation

As the percentage of wind energy on the power grid increases, the intermittent nature of this energy source can make it difficult to keep the generation and the load balanced. While wind speed forecasts can be helpful, they can often be inaccurate. In such cases, we are interested in providing the control room operators additional relevant information they can exploit to make well informed scheduling decisions. In this paper, we investigate if weather conditions in the region of the wind farms can be effective indicators of days when ramp events are likely. Using feature selection techniques from data mining, we show that some variables are more important than others and offer the potential of data-driven predictive models for days with ramp events.