Artificial neural network modeling and sensitivity analysis for soiling effects on photovoltaic panels in Morocco

Laarabi, Bouchra; Tzuc, O. May; Dahlioui, Dounia; Bassam, A.; Flota-Bañuelos, Manuel; Barhdadi, Abdelfettah

doi:10.1016/j.spmi.2017.12.037

Cited by 49 publications

(20 citation statements)

References 22 publications

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“…ANN model with Levenberg‐Marquardt backpropagation algorithm, 6‐35‐1 optimum configuration and Tan sigmoidal as well as Pure linear transfer functions is obtained as the best model with R 2 of 0.928. Sensitivity analysis shows that, soiling rate of photovoltaic glazing is affected most by relative humidity followed by wind direction, wind speed, solar insolation, ambient temperature and rainfall, respectively in the decreasing order of their effect on soiling rate 110 . Solar photovoltaic system is installed in the cladding area of greenhouse by Alonso et al to increase the power production capacity in Almeria, Spain.…”

Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.

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Section: Application Of Ai Techniques In Solar Photovoltaic Systemsmentioning

confidence: 99%

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

2020

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“…Figure 11 shows the scatter plot comparing the real losses and the estimated losses with ANN. Comparing with other models in the literature, (Laarabi et al, 2019) [32] used Iglo, wind speed and direction, Tamb, RH, and rainfall to create a neural network and see the effect of soiling in Morocco, resulting in a neural network with a total of 35 hidden layers, where the RMSE was close to 0.5%, the Mean Absolute Percentage Error (MAPE) greater than 9% and the higher r-value of 0.96. In this sense, the model presented in this work needs two fewer variables (equivalent to a wind sensor), and the number of hidden neurons is reduced, making its execution more efficient and where the results are quite similar.…”

Section: Model 2 Resultsmentioning

confidence: 99%

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

2021

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Fossil fuels and their use to generate energy have multiple disadvantages, with renewable energies being presented as an alternative to this situation. Among them is photovoltaic solar energy, which requires solar installations that are capable of producing energy in an optimal way. These installations will have specific characteristics according to their location and meteorological variables of the place, one of these factors being soiling. Soiling generates energy losses, diminishing the plant’s performance, making it difficult to estimate the losses due to deposited soiling and to measure the amount of soiling if it is not done using very economically expensive devices, such as high-performance particle counters. In this work, these losses have been estimated with artificial intelligence techniques, using meteorological variables, commonly measured in a plant of these characteristics. The study consists of two tests, depending on whether or not the short circuit current (Isc) has been included, obtaining a maximum normalized root mean square error (nRMSE) lower than 7%, a correlation coefficient (R) higher than 0.9, as well as a practically zero normalized mean bias error (nMBE).

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“…• Model structure: Relatively simple structures are most often considered, 63.6% (21 out of 33 models) with 1 hidden layer and the remaining with 2 hidden layers. The number of neurons in the hidden layer is generally less or around 10, while in some models more neurons are selected [36,41,42]. As for the number of parameters, its value is generally less than 500 in the reviewed cases.…”

Section: ) Integration Of Snn In Pv Fddmentioning

confidence: 99%

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Delpha

Diallo

et al. 2021

Renewable and Sustainable Energy Reviews

183

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Artificial neural network modeling and sensitivity analysis for soiling effects on photovoltaic panels in Morocco

Cited by 49 publications

References 22 publications

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

A review on modeling of solar photovoltaic systems using artificial neural networks, fuzzy logic, genetic algorithm and hybrid models

Estimation of Soiling Losses from an Experimental Photovoltaic Plant Using Artificial Intelligence Techniques

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

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