Short-term wind speed forecasting with Markov-switching model

Abstract

A Markov-switching model in wind speed forecasting is examined in this research. The proposed method employs a regime switching process governed by a discrete-state Markov chain to model the nonlinear evolvement of the wind speed time-series. A Bayesian inference rather than the traditional maximum likelihood estimation is applied to evaluate the parameters of the Markov-switching model. Unlike the traditional point forecast of wind speeds, the Markov-switching model can offer both of the point and interval wind speed forecast. To examine the forecasting performance of the Markov-switching model, four wind speed forecasting models, the persistent model, the autoregressive model, the neural networks model, and the Bayesian structural break model, are employed as baselines. Wind speed data collected from utility-scale wind turbines are utilized for the model development and the computational results demonstrate that the Markov-switching model is promising in wind speed forecasting.