Multi-Kalman Filter to Wind Power Forecasting

Wind power forecasting methods are important for the safety of wind renewable energy utilization. However, because wind power is weather dependent and, thus, can be variable and intermittent over different time-scales, it's difficult to build accurate and robust predictive models. In this context, an accurate model is a relevant contribution for a reliable large-scale wind power integration. This paper explores a new approach using a multi-model Kalman filter to provide an estimate of the average hourly wind speed, in a 24 hours horizon. The K-means algorithm is used to obtain the characteristics curves of wind speed each time the sub-model is used for forecasting. The accuracy of the proposed forecasting model is compared with other statistical methods, namely some that are usually considered suitable, robust and accurate.