Modeling of the Photovoltaic Module Operating Temperature for Various Weather Conditions in the Tropical Region

Diouf, Mame Cheikh; Faye, M.; Thiam, Ababacar; Ndiaye, Ababacar; Sabourin, Vincent

doi:10.32604/fdmp.2022.021972

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…Diouf et al [29] analyze the operating temperature of photovoltaic modules as a critical factor that affects their performance. They propose relevant models for the forecast of this operating temperature using ambient temperature and solar irradiance data based on real measurements taken in a tropical region.…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, research works dedicated to studying the effects of meteorological conditions on the internal components of photovoltaic modules were found, which can also affect the performance of forecast models of photovoltaic energy production ( [27][28][29][30][31]). In this same direction, other works point to physical aspects of the manufacture of batteries for photovoltaic energy storage, pointing out its importance to be considered in hybrid models and thus improving forecasting capabilities.…”

Section: Related Workmentioning

confidence: 99%

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Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Castillo-Rojas

Bekios-Calfa

Hernández

2023

International Journal of Photoenergy

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In recent years, photovoltaic energy has become one of the most implemented electricity generation options to help reduce environmental pollution suffered by the planet. Accuracy in this photovoltaic energy forecasting is essential to increase the amount of renewable energy that can be introduced to existing electrical grid systems. The objective of this work is based on developing various computational models capable of making short-term forecasting about the generation of photovoltaic energy that is generated in a solar plant. For the implementation of these models, a hybrid architecture based on recurrent neural networks (RNN) with long short-term memory (LSTM) or gated recurrent units (GRU) structure, combined with shallow artificial neural networks (ANN) with multilayer perceptron (MLP) structure, is established. RNN models have a particular configuration that makes them efficient for processing ordered data in time series. The results of this work have been obtained through controlled experiments with different configurations of its hyperparameters for hybrid RNN-ANN models. From these, the three models with the best performance are selected, and after a comparative analysis between them, the forecasting of photovoltaic energy production for the next few hours can be determined with a determination coefficient of 0.97 and root mean square error (RMSE) of 0.17. It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Castillo-Rojas

Bekios-Calfa

Hernández

2023

International Journal of Photoenergy

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show abstract

“…In the work of Diouf et al [31], the impact of the operating temperature on the performance of photovoltaic modules was analyzed. A temperature forecast model was developed using ambient temperature and solar irradiance data obtained in a tropical region.…”

Section: Related Workmentioning

confidence: 99%

“…In this bibliographical review, another significant aspect stands out: the influence of various factors on the generation of photovoltaic energy [24,[28][29][30][31][32][33]37]. These factors can be categorized into two groups:…”

mentioning

confidence: 99%

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

2023

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In this article, forecast models based on a hybrid architecture that combines recurrent neural networks and shallow neural networks are presented. Two types of models were developed to make predictions. The first type consisted of six models that used records of exported active energy and meteorological variables as inputs. The second type consisted of eight models that used meteorological variables. Different metrics were applied to assess the performance of these models. The best model of each type was selected. Finally, a comparison of the performance between the selected models of both types was presented. The models were validated using real data provided by a solar plant, achieving acceptable levels of accuracy. The selected model of the first type had a root mean square error (RMSE) of 0.19, a mean square error (MSE) of 0.03, a mean absolute error (MAE) of 0.09, a correlation coefficient of 0.96, and a determination coefficient of 0.93. The other selected model of the second type showed lower accuracy in the metrics: RMSE = 0.24, MSE = 0.06, MAE = 0.10, correlation coefficient = 0.95, and determination coefficient = 0.90. Both models demonstrated good performance and acceptable accuracy in forecasting the weekly photovoltaic energy generation of the solar plant.

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A novel thermoelectrical model for dye-sensitized solar cells for consideration of the effects of solar irradiation, wavelength, and surface temperature

Rahmatian,

Sayyaadi,

Ameri

2023

Solar Energy

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Modeling of the Photovoltaic Module Operating Temperature for Various Weather Conditions in the Tropical Region

Cited by 4 publications

References 12 publications

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks

Photovoltaic Energy Forecast Using Weather Data through a Hybrid Model of Recurrent and Shallow Neural Networks

A novel thermoelectrical model for dye-sensitized solar cells for consideration of the effects of solar irradiation, wavelength, and surface temperature

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