Keywords:
Mean Shift, Forecast, Models, Wind energy, Measurement
Abstract
The forecast of wind energy generation is used by many energy companies in order to safely estimate the wind resource and in this way guarantee both the company and the consumer the increase in revenue and the supply of energy from a renewable source over a period of time. Some techniques were developed for this purpose and allow estimating the wind resource and the energy generated for a few hours, days or even weeks. In this work, an information theoretic learning technique for the densification of a wind measurement database is presented, generating new data with the same probability density function of the original set until obtaining the mode of the set, using these virtual data as scenarios of the forecast.
