A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting

Shab Gbémou, Julien Eynard, Stéphane Thil, Emmanuel Guillot and Stéphane Grieu
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Shab Gbémou: Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
Julien Eynard: Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
Stéphane Thil: Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France
Emmanuel Guillot: PROMES-CNRS (UPR 8521), 7 Rue du Four Solaire, 66120 Font-Romeu-Odeillo-Via, France
Stéphane Grieu: Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France

Energies, 2021, vol. 14, issue 11, 1-23

Abstract: The proliferation of photovoltaic (PV) power generation in power distribution grids induces increasing safety and service quality concerns for grid operators. The inherent variability, essentially due to meteorological conditions, of PV power generation affects the power grid reliability. In order to develop efficient monitoring and control schemes for distribution grids, reliable forecasting of the solar resource at several time horizons that are related to regulation, scheduling, dispatching, and unit commitment, is necessary. PV power generation forecasting can result from forecasting global horizontal irradiance (GHI), which is the total amount of shortwave radiation received from above by a surface horizontal to the ground. A comparative study of machine learning methods is given in this paper, with a focus on the most widely used: Gaussian process regression (GPR), support vector regression (SVR), and artificial neural networks (ANN). Two years of GHI data with a time step of 10 min are used to train the models and forecast GHI at varying time horizons, ranging from 10 min to 4 h . Persistence on the clear-sky index, also known as scaled persistence model, is included in this paper as a reference model. Three criteria are used for in-depth performance estimation: normalized root mean square error (nRMSE), dynamic mean absolute error (DMAE) and coverage width-based criterion (CWC). Results confirm that machine learning-based methods outperform the scaled persistence model. The best-performing machine learning-based methods included in this comparative study are the long short-term memory (LSTM) neural network and the GPR model using a rational quadratic kernel with automatic relevance determination.

Keywords: solar resource; global horizontal irradiance; time series forecasting; machine learning; Gaussian process regression; support vector regression; artificial neural networks (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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