Coupling optical and thermal models to accurately predict PV panel electricity production

Hoang, Phuong H.; Bourdin, Vincent; Liu, Q.; Caruso, G.; Archambault, Valérie

doi:10.1016/j.solmat.2013.11.032

Cited by 40 publications

(11 citation statements)

References 10 publications

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“…This equation contains 12 sinusoidal functions and 2 pseudo-periodic functions. But it can be curve fitted to obtain two other simplified equations for the 2013-2023 period [11]. The first equation is: The second expression is based on the equation of the center (C) and the influence of obliquity (R) [13,15].…”

Section: Equation Of Timementioning

confidence: 99%

“…The deterministic component depends only on the distance between the center of Sun and the point of measurement. It is easily identified by the laws of celestial mechanics and energetic physics as explained later in the manuscript [9][10][11][12][13][14][15][16][17][18][19]. In this research study, the deterministic component is calculated using the "clear sky" model.…”

Section: Introductionmentioning

confidence: 99%

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A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

et al. 2018

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Abstract:The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

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Section: Equation Of Timementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

et al. 2018

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“…These coefficients as well as incident solar energy, ambient temperature, ground temperature, layer thermal conductivity, and thickness of each layer, front and back emissivity factors, length and width of the module, working factor, module efficiency, and the thermal coefficients of the module are given to the thermal model as the model inputs (Eqs. (1) to (45)). It should be mentioned that at this step we still need to calculate the convection heat transfer coefficients as the rest inputs of the model.…”

Section: Resultsmentioning

confidence: 99%

Optical and thermal modeling of a photovoltaic module and experimental evaluation of the modeling performance

Abdolzadeh

Zarei

2016

Env Prog and Sustain Energy

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In the present study, coupled optical and thermal models were used to predict the temperature distribution of photovoltaic (PV) module's layers. To verify the modeling performance, an experimental measurement was also conducted in this study and temperatures of two PV modules fixed at 10° and 30° tilt angles were measured and compared with the predicted module temperatures. Two extra experimental data sets measured in two other locations of the globe were also used to further verify the modeling performance. The modeling prediction was also compared with the results of other mathematical models developed in the past studies. This study paid a special attention to the prediction of air convection heat transfer coefficient over the PV module as well as the optical and thermal modeling. The results of this study showed that the present modeling predicts the PV module temperature with a reasonable accuracy compared to the experimental data measured in this study and the other data sets measured in the other parts of the globe. The results also showed that the present modeling predicts the PV module temperature with a higher accuracy compared with the other mathematical models developed in the past studies. Furthermore, this study showed that the accurate prediction of convection heat transfer coefficient over the PV module based on ambient temperature and wind speed, yields to an accurate prediction of PV module temperature © 2016 American Institute of Chemical Engineers Environ Prog, 36: 277–293, 2017

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“…According to Beer-Lambert's law [26], when a ray of light passes through layer i , the rate of attenuation is expressed as:…”

Section: Fig 6 Optical Transfer Process [26]mentioning

confidence: 99%

“…Reflectance, transmittance, and absorbance can be obtained based on above formulas and related references [26,27].…”

Section: Table 3 Related Optical Parametersmentioning

confidence: 99%

An experimental and simulative study on a novel photovoltaic-thermal collector with micro heat pipe array (MHPA-PV/T)

Hou

Quan

Zhao

et al. 2016

Energy and Buildings

105

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Coupling optical and thermal models to accurately predict PV panel electricity production

Cited by 40 publications

References 10 publications

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

Optical and thermal modeling of a photovoltaic module and experimental evaluation of the modeling performance

An experimental and simulative study on a novel photovoltaic-thermal collector with micro heat pipe array (MHPA-PV/T)

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