Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

Hanany Tolba, Nouha Dkhili, Julien Nou, Julien Eynard, Stéphane Thil and Stéphane Grieu
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Hanany Tolba: PROMES-CNRS Laboratory (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Nouha Dkhili: PROMES-CNRS Laboratory (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Julien Nou: PROMES-CNRS Laboratory (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Julien Eynard: PROMES-CNRS Laboratory (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Stéphane Thil: PROMES-CNRS Laboratory (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
Stéphane Grieu: PROMES-CNRS Laboratory (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France

Energies, 2020, vol. 13, issue 16, 1-23

Abstract: In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h , thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting.

Keywords: solar resource; global horizontal irradiance; time series forecasting; machine learning; online Gaussian process regression; online sparse Gaussian process regression (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 (5)

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