Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms
This paper presents a comparative study on artificial neural networks (ANN) and machine learning (ML)-based modelling approaches on experimental data to predict the power output of photovoltaic (PV) systems with the aerosol impact on different types of dust samples such as chalks powder, fly ash, ri...
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Elsevier
2025-06-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025010990 |
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| author | Ashutosh Shukla Rupendra Kumar Pachauri Athar Hussain Ahmed Ali Baseem Khan |
| author_facet | Ashutosh Shukla Rupendra Kumar Pachauri Athar Hussain Ahmed Ali Baseem Khan |
| author_sort | Ashutosh Shukla |
| collection | DOAJ |
| description | This paper presents a comparative study on artificial neural networks (ANN) and machine learning (ML)-based modelling approaches on experimental data to predict the power output of photovoltaic (PV) systems with the aerosol impact on different types of dust samples such as chalks powder, fly ash, rice husks, sand and bricks powder accumulation on the PV module surface. An experimental study deliberates about the maximum power output through the 60 W PV module during the artificial irradiation levels (625W/m2, 675W/m2, 725W/m2, 825W/m2, and 875W/m2) with different dust samples and weights. Both the ANN and support vector regression (SVR) models are trained verified on sample data generated from PV module under controlled laboratory environment. The PV module power output is predicted in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), and R-value to compare the ANN and SVR models' performance. In this context, ANN-based performance metrics in terms of RMSE (1.41), MAPE (11.011), R-Value (0.983), and accuracy (97.02 %) are on the lower side compared to the SVR model as RMSE (0.24), MAPE (1.544), R-Value (0.995), and accuracy (98.3 %). Future utility-scale PV power plants may utilize ANN and SVR-based models for real-time monitoring, predictive maintenance, manual cleaning, and on-site diagnostics. These models complement the experimental setup and provide a scalable, futuristic predictive framework for autonomous, data-driven next-generation solar energy system operation. |
| format | Article |
| id | doaj-art-beb7ed36077e495aa62acb0902c9784f |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-beb7ed36077e495aa62acb0902c9784f2025-08-20T03:10:53ZengElsevierResults in Engineering2590-12302025-06-012610502410.1016/j.rineng.2025.105024Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithmsAshutosh Shukla0Rupendra Kumar Pachauri1Athar Hussain2Ahmed Ali3Baseem Khan4Electrical Cluster, School of Advanced Engineering, UPES, Dehradun, 248007, Uttarakhand, IndiaElectrical Cluster, School of Advanced Engineering, UPES, Dehradun, 248007, Uttarakhand, India; Miyan Research Institute, International University of Business Agriculture and Technology, Dhaka-1230, BangladeshCivil Engineering Department, Netaji Subhas University of Technology, New Delhi, 110078, IndiaDepartment of Electrical and Electronic Engineering Technology, University of Johannesburg, Johannesburg, South AfricaDepartment of Electrical and Computer Engineering, Hawassa University, Hawassa, 05, Ethiopia; Corresponding author.This paper presents a comparative study on artificial neural networks (ANN) and machine learning (ML)-based modelling approaches on experimental data to predict the power output of photovoltaic (PV) systems with the aerosol impact on different types of dust samples such as chalks powder, fly ash, rice husks, sand and bricks powder accumulation on the PV module surface. An experimental study deliberates about the maximum power output through the 60 W PV module during the artificial irradiation levels (625W/m2, 675W/m2, 725W/m2, 825W/m2, and 875W/m2) with different dust samples and weights. Both the ANN and support vector regression (SVR) models are trained verified on sample data generated from PV module under controlled laboratory environment. The PV module power output is predicted in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), and R-value to compare the ANN and SVR models' performance. In this context, ANN-based performance metrics in terms of RMSE (1.41), MAPE (11.011), R-Value (0.983), and accuracy (97.02 %) are on the lower side compared to the SVR model as RMSE (0.24), MAPE (1.544), R-Value (0.995), and accuracy (98.3 %). Future utility-scale PV power plants may utilize ANN and SVR-based models for real-time monitoring, predictive maintenance, manual cleaning, and on-site diagnostics. These models complement the experimental setup and provide a scalable, futuristic predictive framework for autonomous, data-driven next-generation solar energy system operation.http://www.sciencedirect.com/science/article/pii/S2590123025010990Photovoltaic systemDust effectNeural networkSupport vector regressionPower loss |
| spellingShingle | Ashutosh Shukla Rupendra Kumar Pachauri Athar Hussain Ahmed Ali Baseem Khan Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms Results in Engineering Photovoltaic system Dust effect Neural network Support vector regression Power loss |
| title | Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms |
| title_full | Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms |
| title_fullStr | Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms |
| title_full_unstemmed | Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms |
| title_short | Comparative analysis dust accumulation impact on PV performance using artificial neural network and machine learning algorithms |
| title_sort | comparative analysis dust accumulation impact on pv performance using artificial neural network and machine learning algorithms |
| topic | Photovoltaic system Dust effect Neural network Support vector regression Power loss |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025010990 |
| work_keys_str_mv | AT ashutoshshukla comparativeanalysisdustaccumulationimpactonpvperformanceusingartificialneuralnetworkandmachinelearningalgorithms AT rupendrakumarpachauri comparativeanalysisdustaccumulationimpactonpvperformanceusingartificialneuralnetworkandmachinelearningalgorithms AT atharhussain comparativeanalysisdustaccumulationimpactonpvperformanceusingartificialneuralnetworkandmachinelearningalgorithms AT ahmedali comparativeanalysisdustaccumulationimpactonpvperformanceusingartificialneuralnetworkandmachinelearningalgorithms AT baseemkhan comparativeanalysisdustaccumulationimpactonpvperformanceusingartificialneuralnetworkandmachinelearningalgorithms |