Multitask support vector regression for solar and wind energy prediction
EntityUAM. Departamento de Ingeniería Informática
10.3390/en13236308Energies 13.23 (2020): 6308
Funded byThe authors acknowledge financial support from Spain’s grants TIN2016-76406-P and PID2019-106827GB- I00/AEI/10.13039/501100011033. Work supported also by The first author is also supported by a FPU–UAM grant
ProjectGobierno de España. TIN2016-76406-P; Gobierno de España. PID2019-106827GB- I00
SubjectsMulti-task learning; Photovoltaic energy; Support vector regression; Wind energy; Informática
Rights© The author(s)
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
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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