Improving wind power prediction with retraining machine learning algorithms

2018 ◽  
Author(s):  
Mariam Barque ◽  
Simon Martin ◽  
Jeremie Etienne Norbert Vianin ◽  
Dominique Genoud ◽  
David Wannier
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Power Prediction ◽  
Wind Power Prediction

scholarly journals Wind power plant forecasting and power prediction methods using Machine Learning Algorithms

2021 ◽  
Vol 10 (2) ◽  
pp. 687-691
Keyword(s):  
Machine Learning ◽  
Wind Speed ◽  
Wind Energy ◽  
Wind Power ◽  
Renewable Energy Sources ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Power Prediction ◽  
Wind Speed Data

A significant and eligible source such as wind energy has the potential for producing energy in a continuous and sustainable manner among renewable energy sources. However, wind energy has several challenges, such as initial investment costs, the stationary property of wind plants, and the difficulty in finding wind-efficient energy areas. In this study, wind power forecasting was performed based on daily wind speed data using machine learning algorithms. The proposed method is based on machine learning algorithms to forecast wind power values efficiently. Tests were conducted on data sets to reveal performances of machine learning algorithms. The results showed that machine learning algorithms could be used for forecasting long-term wind power values with respect to historical wind speed data. Furthermore, several machine learning models were built for analysis on the accuracy level of the respective models, i.e, the accuracy levels of the machine.

scholarly journals An Evaluation on Diverse Machine Learning Algorithms for Hourly Univariate Wind Power Prediction in the Arctic

2021 ◽  
Vol 2141 (1) ◽  
pp. 012016
Author(s):  
Hao Chen ◽  
Yngve Birkelund
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Arctic ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Forecasting ◽  
Arctic Regions ◽  
Power Forecasting

Abstract Wind power forecasting is crucial for wind power systems, grid load balance, maintenance, and grid operation optimization. The utilization of wind energy in the Arctic regions helps reduce greenhouse gas emissions in this environmentally vulnerable area. In the present study, eight various models, seven of which are representative machine learning algorithms, are used to make 1, 2, and 3 step hourly wind power predictions for five wind parks inside the Norwegian Arctic regions, and their performance is compared. Consequently, we recommend the persistence model, multilayer perceptron, and support vector regression for univariate time-series wind power forecasting within the time horizon of 3 hours.

Machine learning ensembles for wind power prediction

2016 ◽  
Vol 89 ◽  
pp. 671-679 ◽  
Author(s):  
Justin Heinermann ◽  
Oliver Kramer
Keyword(s):  
Machine Learning ◽  
Wind Power ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Learning Ensembles
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