Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches
Abstract
:1. Introduction
2. Research Method and Experimental Set-Ups
3. Machine Learning Approach for Prediction of Output Power
3.1. Support Vector Machine Regression (SVMR)
3.2. Gaussian Regression (GR)
3.3. Multiple Linear Regression (MLR)
4. Prediction of Output Power Due to Size-Varied Dust Pollutants Deposition
5. Establishing the Relationship between Solar Radiation and Short-Circuit Current
6. Analysis of PV Panel Performance under the Size-Varied Dust Pollutants Deposition
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Sample Availability
References
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Nature of Study | Work | Measure Parameter | Outcomes | References |
---|---|---|---|---|
Outdoor | Effect of dust discoloration on PV panel material | Glass Transmittance | 15% degradation | [20] |
Indoor | Effect of various types of dust deposition on mono and poly PV panel performance | Output power | Mono—12% degradation Poly—5% degradation For Ash deposition | [21] |
Indoor | Type of dust impact on PV panel performance | Efficacy | Dust degrades the panel efficacy | [22] |
Outdoor | Effect of desert dust on PV panels | Output power | 98.13% reduction | [23] |
Indoor | Investigation of PV efficacy under the deposition of different types of dust pollutants | Power | 99.76% Performance reduction due to carbon dust | [24] |
Outdoor | Proposed a model to encounter the impact of dust on grid-connected PV system | Inverter efficacy (IE) and performance efficacy (PE) | 94% IE 73% PE | [25] |
Indoor | Study the impact of coal dust | Output power | 62.05% degradation | [26] |
Sl. No. | Particle Size Range | Symbolic Representation | Designated As |
---|---|---|---|
1 | 600 µ to 850 µ | T1 | Larger size |
2 | 300 µ to 600 µ | T2 | Moderate larger size |
3 | 150 µ to 300 µ | T3 | Medium size |
4 | 75 µ to 150 µ | T4 | Moderate medium size |
5 | less than 75 µ | T5 | Smaller size |
Algorithms | Performance Parameters | Fold-1 | Fold-2 | Fold-3 | Average Value of Performance Parameters |
---|---|---|---|---|---|
SVMR | MAE | 0.1708 | 0.1708 | 0.1351 | 0.1589 |
MSE | 0.0381 | 0.0381 | 0.0224 | 0.0328 | |
R2 | 0.9910 | 0.9910 | 0.9937 | 0.9919 | |
MLR | MAE | 0.3926 | 0.3926 | 0.3256 | 0.3702 |
MSE | 0.2833 | 0.2833 | 0.2377 | 0.2681 | |
R2 | 0.9334 | 0.9334 | 0.9334 | 0.9334 | |
GR | MAE | 0.6301 | 0.6301 | 0.5773 | 0.6125 |
MSE | 0.4906 | 0.4906 | 0.4523 | 0.4778 | |
R2 | 0.8847 | 0.8847 | 0.8732 | 0.8808 |
Solar Irradiance (W/m2) | Maximum Power Output (W) | Short-Circuit Current (A) | Open-Circuit Voltage (V) |
---|---|---|---|
449.58 | 3.42 | 0.27 | 18.85 |
516.22 | 3.73 | 0.31 | 19.15 |
593.70 | 4.35 | 0.35 | 19.35 |
608.63 | 4.97 | 0.37 | 19.50 |
661.49 | 5.13 | 0.39 | 19.95 |
719.60 | 6.18 | 0.44 | 20.10 |
750.50 | 6.71 | 0.48 | 20.30 |
788.49 | 6.89 | 0.49 | 20.60 |
878.67 | 7.24 | 0.54 | 20.75 |
918.10 | 8.48 | 0.63 | 20.95 |
Solar Irradiance (G) | Short-Circuit Current (ISC) | G2 | ISC × G |
---|---|---|---|
449.58 | 0.27 | 202,122.20 | 160.03 |
516.22 | 0.31 | 266,483.10 | 207.80 |
593.70 | 0.35 | 352,479.70 | 225.19 |
608.63 | 0.37 | 370,430.50 | 257.98 |
661.49 | 0.39 | 437,569.00 | 386.36 |
719.60 | 0.44 | 517,824.20 | 474.48 |
750.00 | 0.48 | 548,147.70 | 325.76 |
788.49 | 0.49 | 621,716.50 | 121.39 |
878.67 | 0.54 | 772,061.00 | 345.41 |
918.10 | 0.63 | 842,907.60 | 578.40 |
∑G = 6884.48 | ∑ISC = 4.27 | ∑G2 = 493,1742 | ∑ISC × G = 3082.8 |
Sl. No | Parameter | Values |
---|---|---|
1 | Coefficient of determination | 0.96124 |
2 | Standard error | 0.0230 |
4 | Standard deviation of residual | 0.0217 |
Observation | Observed Value | Predicted Value | Residuals (Observed Value-Predicted Value) | Standard Residuals (Residual/Standard Deviationof the Residual) |
---|---|---|---|---|
1 | 0.27 | 0.256448069 | 0.013551931 | 0.623200484 |
2 | 0.31 | 0.304221679 | 0.005778321 | 0.265722458 |
3 | 0.35 | 0.359766386 | −0.009766386 | −0.449118053 |
4 | 0.37 | 0.370469568 | −0.000469568 | −0.021593587 |
5 | 0.39 | 0.408364421 | −0.018364421 | −0.84450815 |
6 | 0.44 | 0.464912776 | −0.024912776 | −1.145641458 |
7 | 0.48 | 0.450022951 | 0.029977049 | 1.378527641 |
8 | 0.49 | 0.499409566 | −0.009409566 | −0.432709265 |
9 | 0.54 | 0.564058788 | −0.024058788 | −1.106369887 |
10 | 0.63 | 0.592325796 | 0.037674204 | 1.732489817 |
Dust Particle Size (micron) | Short-Circuit Current (amp) | Reduction in Short-Circuit Current (%) |
---|---|---|
T0 | 0.50 | NA |
T1 | 0.43 | 15.68 |
T2 | 0.37 | 27.45 |
T3 | 0.31 | 39.21 |
T4 | 0.28 | 45.09 |
T5 | 0.26 | 49.01 |
Pollutant Size (µ) | Simulated Solar Irradiance GS (W/m2) | Reduction in Solar Irradiance RSI (%) |
---|---|---|
600–850 µ | 702.85 | 12.14 |
300–600 µ | 617.14 | 22.85 |
150–300 µ | 531.43 | 33.57 |
75–150 µ | 488.57 | 38.92 |
Less than 75 µ | 460.00 | 42.50 |
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Tripathi, A.K.; Aruna, M.; Venkatesan, E.P.; Abbas, M.; Afzal, A.; Shaik, S.; Linul, E. Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches. Molecules 2022, 27, 7853. https://doi.org/10.3390/molecules27227853
Tripathi AK, Aruna M, Venkatesan EP, Abbas M, Afzal A, Shaik S, Linul E. Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches. Molecules. 2022; 27(22):7853. https://doi.org/10.3390/molecules27227853
Chicago/Turabian StyleTripathi, Abhishek Kumar, Mangalpady Aruna, Elumalai Perumal Venkatesan, Mohamed Abbas, Asif Afzal, Saboor Shaik, and Emanoil Linul. 2022. "Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches" Molecules 27, no. 22: 7853. https://doi.org/10.3390/molecules27227853