Dust analysis in photo-voltaic solar plants with satellite data

Velásquez, Ricardo Manuel Arias; Ezcurra, Tamara Tatiana Pando

doi:10.1016/j.asej.2023.102314

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The performance indexes of the proposed BNN model are R, RMSE, MAE and MAPE, at 99.96%, 0.22, 0.08 and 2.3, respectively. Valasquez and Ezcurra [17] used a random forest model to estimate production loss due to dust. The MAE, mean square error (MSE) and R 2 of the proposed algorithm are 0.22, 0.07 and 0.88, respectively.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Tanyıldızı Ağır

2024

Sustainability

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Sustainable and renewable energy sources are of great importance in today’s world. In this respect, renewable energy sources are used in many fields of technology. In order to minimize dust on PV panels and ensure their sustainability, power losses due to dust must be estimated accurately. In this way, the efficiency of a sustainable energy source will increase and serious economic savings can be achieved. In this study, a hybrid deep learning model was designed to predict losses caused by dust in PV panels installed in the Manisa Saruhanlı district. The hybrid deep learning model consists of Long Short-Term Memory (LSTM) and K-Nearest-Neighbors (KNN) algorithms. The performance of the proposed hybrid deep learning model was compared with LSTM and KNN algorithms. Sensitivity analysis was performed to statistically evaluate the prediction results. The input variables of the models were time, sunshine duration, humidity, ambient temperature and solar radiation. The output variable was the losses caused by dust in the PV panels. Hybrid LSTM-KNN, LSTM and KNN models predicted losses caused by dust in PV panels with 98.22%, 95.51% and 61.49% accuracy. The hybrid LSTM-KNN model predicted losses caused by dust in PV panels with higher accuracy than other models. Using LSTM and KNN algorithms together improved the performance of the hybrid deep learning model. With sensitivity analysis, it was found that solar radiation is the most important variable affecting the losses caused by dust in PV panels.

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Section: Introductionmentioning

confidence: 99%

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Tanyıldızı Ağır

2024

Sustainability

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A holistic review of the effects of dust buildup on solar photovoltaic panel efficiency

Yakubu,

Samikannu,

Gawusu

et al. 2025

Solar Compass

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Review and Comparison of Methods for Soiling Modeling in Large Grid-Connected PV Plants

Redondo,

Platero,

Moset

et al. 2024

Sustainability

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Soiling in PV modules is one of the biggest issues affecting performance and economic losses in PV power plants; thus, it is essential to supervise and forecast soiling profiles and establish the best cleaning program. This paper analyzes different methods for soiling modeling in Large Grid-Connected PV Plants and discusses the different factors influencing soiling. Analytical models from environmental conditions are discussed in detail, comparing the proposed model by the authors (SOMOSclean) with another three relevant models from the literature (Kimber, HSU, and Toth), applying them to 16 PV power plants in Spain (total capacity of 727 MWp). Uncertainty between models and sensors is also measured, presenting the numerical results for a period of 2 years. While simpler models may offer straightforward implementation, they often fail to capture the full complexity of soiling dynamics, leading to increased RMSE error.

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Dust analysis in photo-voltaic solar plants with satellite data

Cited by 3 publications

References 8 publications

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

A holistic review of the effects of dust buildup on solar photovoltaic panel efficiency

Review and Comparison of Methods for Soiling Modeling in Large Grid-Connected PV Plants

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