scholarly journals Wind energy prediction with LS-SVM based on Lorenz perturbation

2017 ◽  
Vol 2017 (13) ◽  
pp. 1724-1727 ◽  
Author(s):  
Yagang Zhang ◽  
Penghui Wang ◽  
Chenhong Zhang ◽  
Shuang Lei
Keyword(s):  
Wind Energy ◽  
Energy Prediction

Wind energy prediction and monitoring with neural computation

2013 ◽  
Vol 109 ◽  
pp. 84-93 ◽  
Author(s):  
Oliver Kramer ◽  
Fabian Gieseke ◽  
Benjamin Satzger
Keyword(s):  
Wind Energy ◽  
Neural Computation ◽  
Energy Prediction

scholarly journals Multitask Support Vector Regression for Solar and Wind Energy Prediction

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6308
Author(s):  
Carlos Ruiz ◽  
Carlos M. Alaíz ◽  
José R. Dorronsoro
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Support Vector Regression ◽  
Energy Production ◽  
Multitask Learning ◽  
Support Vector ◽  
Prediction Problem ◽  
Energy Prediction ◽  
The Common ◽  
The Impact

Given the impact of renewable sources in the overall energy production, accurate predictions are becoming essential, with machine learning becoming a very important tool in this context. In many situations, the prediction problem can be divided into several tasks, more or less related between them but each with its own particularities. Multitask learning (MTL) aims to exploit this structure, training several models at the same time to improve on the results achievable either by a common model or by task-specific models. In this paper, we show how an MTL approach based on support vector regression can be applied to the prediction of photovoltaic and wind energy, problems where tasks can be defined according to different criteria. As shown experimentally with three different datasets, the MTL approach clearly outperforms the results of the common and specific models for photovoltaic energy, and are at the very least quite competitive for wind energy.

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