Short-term wind speed forecasting by spectral analysis from long-term observations with missing values

Abstract

In this paper, we propose a novel wind speed forecasting framework. The performance of the proposed framework is assessed on the wind speed measurements collected from the five meteorological stations in the Marmara region of Turkey. The experimental results show that trimming of the diurnal, the weekly, the monthly, and the annual patterns in the measurements significantly enhances the estimation accuracy. The proposed framework builds on data de-trending, covariance-factorization via a recently developed subspace method, and one-step-ahead and/or multi-step-ahead Kalman filter prediction ideas. The data sets do not have to be complete. In fact, as in sensor failures, intermittently or sequentially missing measurements are permitted. The numerical experiments on the real data sets show that the wind speed forecasts, in particular the multi-step-ahead forecasts, outperform the benchmark values computed with the persistence forecasting models by a clear difference.