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Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines

Fahim, Muhammad; Sharma, Vishal; Cao, Tuan-Vu; Canberk, Berk; Duong, Trung Q.


Muhammad Fahim

Vishal Sharma

Tuan-Vu Cao

Trung Q. Duong


Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and power generation prediction is a complex problem due to the unpredictability of wind speed. Consequently, it limits the decision power of the management team to plan the energy consumption in an effective way. Our proposed model solves this challenge by utilizing a 5G-Next Generation-Radio Access Network (5G-NG-RAN) assisted cloud-based digital twins’ framework to virtually monitor wind turbines and form a predictive model to forecast wind speed and predict the generated power. The developed model is based on Microsoft Azure digital twins infrastructure as a 5-dimensional digital twins platform. The predictive modeling is based on a deep learning approach, temporal convolution network (TCN) followed by a non-parametric k-nearest neighbor (kNN) regression. Predictive modeling has two components. First, it processes the univariate time series data of wind to predict its speed. Secondly, it estimates the power generation for each quarter of the year ranges from one week to a whole month (i.e., medium-term prediction) To evaluate the framework the experiments are performed on onshore wind turbines publicly available datasets. The obtained results confirm the applicability of the proposed framework. Furthermore, the comparative analysis with the existing classical prediction models shows that our designed approach obtained better results. The model can assist the management team to monitor the wind farms remotely as well as estimate the power generation in advance.

Journal Article Type Article
Acceptance Date Jan 25, 2022
Online Publication Date Jan 28, 2022
Publication Date 2022
Deposit Date Nov 1, 2022
Publicly Available Date Nov 2, 2022
Journal IEEE Access
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 10
Pages 14184-14194
Public URL


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