Abstract
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.
Highlights
Due to the high increase of petroleum prices and imposed politics on industrial countries to reduce CO2 levels, the use of renewable sources to produce energy has become an obligation.different solutions are used to cover energy needs while respecting clean and eco-friendly requirements [1]
In order to assess the performance of the classification algorithms based on machine learning (ML) and the proposed approach, we introduce the accuracy index, the confusion matrix, the receiver operating characteristic (ROC) curve, and the normalized mean absolute error (NMAE)
In order to address the performance of both SDMs and diode models (DDMs) under different levels of irradiance and temperature, we identify the low values class when the irradiance is below 400 W/m2, the medium is between 400 W/m2 and 800 W/m2 and the high values class is above 800 W/m2
Summary
Due to the high increase of petroleum prices and imposed politics on industrial countries to reduce CO2 levels, the use of renewable sources to produce energy has become an obligation. Different solutions are used to cover energy needs while respecting clean and eco-friendly requirements [1]. Wind, hydro, and solar sources of energy show an appropriate solution ensuring green electricity for diverse industrial and domestic applications. The modeling task is a substantial procedure to analyze the electrical performances of the photovoltaic (PV) cell/module/array. Equivalent-circuit models are mainly implemented to predict the long-term potential of the photovoltaic device. Single-diode model (SDMs) and double-diode models (DDMs) represent the most used configurations [3]. The single-diode model is less complicated compared to the double-diode configuration; this is because of the limited number of parameters needed.
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