IMS Center Article on Wind Speed Prediction Published in Renewable Energy

A new article from the IMS Center focuses on a novel method for using indirect information to enhance wind speed prediction accuracy—an important goal in the Wind Turbine industry. This article was published in Renewable Energy, a highly impactful international journal. 

IMS Center researchers Wenzhe Li and Yinglu Wang, along with post-doc Xiang Li, Research Assistant Professor Xiaodong Jia, and Center Director, Professor Jay Lee, composed this compelling article, which can be read in its entirety here.  


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.

Benefits & Impacts

The methodology presented in this article seeks to address critical challenges in the wind energy industry in accurately predicting wind speed, which is key in projecting energy output, as well as identifying potential surges and wind ramp-ups.

This method for incorporating suppementary information in the model development process could easily be applied to other applications in the renewable energy industry, as well as applications in numerous other industires, further broadening its impact.

To see a complete list of the Center's publications, please visit our publication section here.


Featured in this Article
Professor Jay Lee
Wenzhe Li
Dr. Xiaodong Jia
Xiang Li.jpg
Dr. Xiang Li
Yinglu Wang