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Open AccessFeature PaperArticle

Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions

1
School of Electrical and Computer Engineering, National Technical University of Athens 15780, Greece
2
SmartiLab Laboratory, Moroccan School of Engineering Sciences (EMSI), Rabat 10090, Morocco
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(2), 315; https://doi.org/10.3390/electronics9020315
Received: 27 December 2019 / Revised: 24 January 2020 / Accepted: 28 January 2020 / Published: 12 February 2020
(This article belongs to the Section Power Electronics)
It is important to investigate the long-term performances of an accurate modeling of photovoltaic (PV) systems, especially in the prediction of output power, with single and double diode models as the configurations mainly applied for this purpose. However, the use of one configuration to model PV panel limits the accuracy of its predicted performances. This paper proposes a new hybrid approach based on classification algorithms in the machine learning framework that combines both single and double models in accordance with the climatic condition in order to predict the output PV power with higher accuracy. Classification trees, k-nearest neighbor, discriminant analysis, Naïve Bayes, support vector machines (SVMs), and classification ensembles algorithms are investigated to estimate the PV power under different conditions of the Mediterranean climate. The examined classification algorithms demonstrate that the double diode model seems more relevant for low and medium levels of solar irradiance and temperature. Accuracy between 86% and 87.5% demonstrates the high potential of the classification techniques in the PV power predicting. The normalized mean absolute error up to 1.5% ensures errors less than those obtained from both single-diode and double-diode equivalent-circuit models with a reduction up to 0.15%. The proposed hybrid approach using machine learning (ML) algorithms could be a key solution for photovoltaic and industrial software to predict more accurate performances.
Keywords: PV modules modeling; equivalent-circuit models; prediction of performances; machine learning; classification algorithms PV modules modeling; equivalent-circuit models; prediction of performances; machine learning; classification algorithms
MDPI and ACS Style

Maria, M.; Yassine, C. Machine Learning Based Approaches for Modeling the Output Power of Photovoltaic Array in Real Outdoor Conditions. Electronics 2020, 9, 315.

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