A Markov model for short term wind speed prediction by integrating the wind acceleration information

Wind speed prediction is an important research topic in the wind industry and many algorithms have been proposed to fulfill the prediction tasks. By reviewing the existing methods, one can find that the supplemental information, such as acceleration and turbulence intensity, that can be indirectly derived from wind speed is still less considered in the prediction models. To make a better utilization of these indirect information and future enhance the prediction accuracies, this paper proposes a novel Markov prediction model to integrate the wind acceleration. The proposes method first encodes the wind speed sequence into a discrete state sequence based on a 2D codebook that associates with the joint distribution of speed and acceleration. The discrete state sequence is then utilized to compute the state Transition Probably Matrix (TPM). The TPM governs the underlying state transition mechanisms in the Markov chain (or the state sequence) and serves as key to predict the states into the future time horizon. Lastly, the predicted states sequences are decoded into wind speed and the prediction uncertainty can be described as predictive distributions. The proposed method holds several advantages such as enhanced prediction accuracy and excellent flexibility to encode the supplemental information into the prediction model. The effectiveness of the proposed method is verified in the case studies by benchmarking with existing methods. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Wind speed prediction is crucial in the wind industry, with insufficient attention to supplemental information in existing models. This paper introduces a novel Markov prediction model integrating wind acceleration, encoding sequences to compute Transition Probably Matrix for accurate predictions and uncertainty descriptions. The proposed method demonstrates enhanced accuracy and flexibility in encoding supplemental information, verified through benchmarking with existing methods.