Multitask Support Vector Regression for Solar and Wind Energy Prediction

Carlos Ruiz, Carlos M. Alaíz and José R. Dorronsoro
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Carlos Ruiz: Department of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, Spain
Carlos M. Alaíz: Department of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, Spain
José R. Dorronsoro: Department of Computer Engineering, Universidad Autónoma de Madrid, 28049 Madrid, Spain

Energies, 2020, vol. 13, issue 23, 1-21

Abstract: 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.

Keywords: wind energy; photovoltaic energy; support vector regression; multi-task learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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