DOI: https://doi.org/10.14710/ijred.2022.42195

Artificial Neural Network Prediction Model of Dust Effect on Photovoltaic Performance for Residential applications: Malaysia Case Study

1Solar Energy Research Institute, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

2Faculty of Electrical & Electronic Engineering Technology, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia

3Faculty of Computer and Mathematical Science, Universiti Teknologi MARA, Perlis Branch, Perlis, Malaysia

Received: 22 Oct 2021; Revised: 12 Dec 2021; Accepted: 24 Dec 2021; Available online: 8 Jan 2022; Published: 5 May 2022.
Editor(s): H. Hadiyanto
Open Access Copyright (c) 2022 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract
Dust accumulation on the photovoltaic system adversely degrades its power conversion efficiency (PCE). Focusing on residential installations, dust accumulation on PV modules installed in tropical regions may be vulnerable due to lower inclination angles and rainfall that encourage dust settlement on PV surfaces. However, most related studies in the tropics are concerned with studies in the laboratory, where dust collection is not from the actual field, and an accurate performance prediction model is impossible to obtain. This paper investigates the dust-related degradation in the PV output performance based on the developed Artificial Neural Network (ANN) predictive model. For this purpose, two identical monocrystalline modules of 120 Wp were tested and assessed under real operating conditions in Melaka, Malaysia (2.1896° N, 102.2501° E), of which one module was dust-free (clean). At the same time, the other was left uncleaned (dusty) for one month. The experimental datasets were divided into three sets: the first set was used for training and testing purposes, while the second and third, namely Data 2 and Data 3, were used for validating the proposed ANN model. The accuracy study shows that the predicted data using the ANN model and the experimentally acquired data are in good agreement, with MAE and RMSE for the cleaned PV module are as low as 1.28 °C, and 1.96 °C respectively for Data 2 and 3.93 °C and 4.92 °C respectively for Data 3.  Meanwhile, the RMSE and MAE for the dusty PV module are 1.53°C and 2.82 °C respectively for Data 2 and 4.13 °C and 5.26 °C for Data 3. The ANN predictive model was then used for yield forecasting in a residential installation and found that the clean PV system provides a 7.29 % higher yield than a dusty system. The proposed ANN model is beneficial for PV system installers to assess and anticipate the impacts of dust on the PV installation in cities with similar climatic conditions.
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Keywords: Dust effect; PV performance; ANN; Yield forecasting; Electrical output

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Section: Original Research Article
Language : EN
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  1. Ahmad, E.Z., Sopian, K., Jarimi, H., Fazlizan, A., Elbreki, A., Hamid, A.S.A., Rostami, S. & Ibrahim, A. 2021. Recent advances in passive cooling methods for photovoltaic performance enhancement. International Journal of Electrical and Computer Engineering 11(1): 146–154. http://doi.org/10.11591/ijece.v11i1.pp146-154
  2. Al-Addous, M., Dalala, Z., Alawneh, F. & Class, C.B. 2019. Modeling and quantifying dust accumulation impact on PV module performance. Solar Energy 194(September): 86–102. https://doi.org/10.1016/j.solener.2019.09.086
  3. Al-Kouz, W., Al-Dahidi, S., Hammad, B. & Al-Abed, M. 2019. Modeling and analysis framework for investigating the impact of dust and temperature on PV systems’ performance and optimum cleaning frequency. Applied Sciences (Switzerland) https://doi.org10.3390/app9071397
  4. Al Siyabi, I., Al Mayasi, A., Al Shukaili, A. & Khanna, S. 2021. Effect of soiling on solar photovoltaic performance under desert climatic conditions. Energies 14(3) https://doi.org/10.3390/en14030659
  5. Andrea, Y., Pogrebnaya, T. & Kichonge, B. 2019. Effect of Industrial Dust Deposition on Photovoltaic Module Performance: Experimental Measurements in the Tropical Region. International Journal of Photoenergy 2019 https://doi.org/10.1155/2019/1892148
  6. Bouaichi, A., Merrouni, A.A., Hajjaj, C., Zitouni, H., Ghennioui, A., El Amrani, A. & Messaoudi, C. 2019. In-situ inspection and measurement of degradation mechanisms for crystalline and thin film PV systems under harsh climatic conditions. Energy Procedia 157: 1210–1219. https://doi.org/10.1016/j.egypro.2018.11.287
  7. Chen, C., Twycross, J. & Garibaldi, J.M. 2017. A new accuracy measure based on bounded relative error for time series forecasting. PLoS ONE https://doi.org/10.1371/journal.pone.0174202
  8. Chiteka, K., Arora, R. & Sridhara, S.N. 2020. A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models. Energy Systems 11(4): 981–1002. https://doi.org/10.1007/s12667-019-00348-w
  9. Coello, M. & Boyle, L. 2019. Simple Model for Predicting Time Series Soiling of Photovoltaic Panels. IEEE Journal of Photovoltaics 9(5): 1382–1387
  10. Conceição, R., Silva, H.G., Mirão, J., Gostein, M., Fialho, L., Narvarte, L. & Collares-Pereira, M. 2018. Saharan dust transport to Europe and its impact on photovoltaic performance: A case study of soiling in Portugal. Solar Energy 160(November 2017): 94–102. https://doi.org/10.1016/j.solener.2017.11.059
  11. Fan, S., Wang, Y., Cao, S., Sun, T. & Liu, P. 2021. A novel method for analyzing the effect of dust accumulation on energy efficiency loss in photovoltaic (PV) system. Energy 234: 121112. https://doi.org/10.1016/j.energy.2021.121112
  12. Green, M.A. 2019. Photovoltaic technology and visions for the future. Progress in Energy https://doi.org/10.1088/2516-1083/ab0fa8
  13. Günther, F. & Fritsch, S. 2010. Neuralnet: Training of neural networks. R Journal 2(1): 30–38
  14. Hammad, B., Al-Abed, M., Al-Ghandoor, A., Al-Sardeah, A. & Al-Bashir, A. 2018. Modeling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study. Renewable and Sustainable Energy Reviews 82(April 2017): 2218–2234. https://doi.org/10.1016/j.rser.2017.08.070
  15. Jamil, W.J., Rahman, H.A., Shaari, S. & Desa, M.K.M. 2020. Modeling of Soiling Derating Factor in Determining Photovoltaic Outputs. IEEE Journal of Photovoltaics 10(5): 1417–1423. https://doi.org/10.1109/JPHOTOV.2020.3003815
  16. Jarimi, H., Aydin, D., Bakar, M.N.A., Ibrahim, A., Fazlizan, A., Safwan, A.A. & Sopian, K. 2021. Exergy-based sustainability analysis of a dual fluid hybrid photovoltaic and thermal solar collector. International Journal of Exergy 35(3): 358–373. https://doi.org/10.1504/IJEX.2021.115903
  17. Jaszczur, M., Teneta, J., Styszko, K., Hassan, Q., Burzyńska, P., Marcinek, E. & Łopian, N. 2019. The field experiments and model of the natural dust deposition effects on photovoltaic module efficiency. Environmental Science and Pollution Research 26(9): 8402–8417
  18. Kaldellis, J.K. & Kapsali, M. 2011. Simulating the dust effect on the energy performance of photovoltaic generators based on experimental measurements. Energy 36(8): 5154–5161. http://dx.doi.org/10.1016/j.energy.2011.06.018
  19. Kazem, H.A., Chaichan, M.T., Al-Waeli, A.H.A. & Sopian, K. 2020a. A review of dust accumulation and cleaning methods for solar photovoltaic systems. Journal of Cleaner Production 276: 123187. https://doi.org/10.1016/j.jclepro.2020.123187
  20. Kazem, H.A., Chaichan, M.T., Al-Waeli, A.H.A. & Sopian, K. 2020b. A novel model and experimental validation of dust impact on grid-connected photovoltaic system performance in Northern Oman. Solar Energy 206(June): 564–578. https://doi.org/10.1016/j.solener.2020.06.043
  21. Klugmann-Radziemska, E. 2015. Degradation of electrical performance of a crystalline photovoltaic module due to dust deposition in northern Poland. Renewable Energy 78: 418–426. http://dx.doi.org/10.1016/j.renene.2015.01.018
  22. Li, W., Wu, X., Jiao, W., Qi, G. & Liu, Y. 2017. Modelling of dust removal in rotating packed bed using artificial neural networks (ANN). Applied Thermal Engineering 112: 208–213. http://dx.doi.org/10.1016/j.applthermaleng.2016.09.159
  23. Mani, M. & Pillai, R. 2010. Impact of dust on solar photovoltaic (PV) performance: Research status, challenges and recommendations. Renewable and Sustainable Energy Reviews 14(9): 3124–3131. http://dx.doi.org/10.1016/j.rser.2010.07.065
  24. Mittal, M., Bora, B., Saxena, S. & Gaur, A.M. 2018. Performance prediction of PV module using electrical equivalent model and artificial neural network. Solar Energy 176(October): 104–117. https://doi.org/10.1016/j.solener.2018.10.018
  25. Oh, S. 2019. Analytic and Monte-Carlo studies of the effect of dust accumulation on photovoltaics. Solar Energy 188(July): 1243–1247. https://doi.org/10.1016/j.solener.2019.07.011
  26. Omar, Ahmad Maliki, S.Shaari, S.I.S. 2012. Grid-connected photovoltaic systems design. Vol.1. Sustainable Energy Development Authority (SEDA), Putrajaya, Malaysia
  27. Omar, A.M. & Shaari, S. 2009. Sizing verification of photovoltaic array and grid-connected inverter ratio for the Malaysian building integrated photovoltaic project. International Journal of Low-Carbon Technologies 4(4): 254–257. https://doi.org/10.1093/ijlct/ctp033
  28. Rajput, P., Malvoni, M., Kumar, N.M., Sastry, O.S. & Tiwari, G.N. 2019. Risk priority number for understanding the severity of photovoltaic failure modes and their impacts on performance degradation. Case Studies in Thermal Engineering 16(October): 100563. https://doi.org/10.1016/j.csite.2019.100563
  29. Ramadan, O., Omer, S., Ding, Y., Jarimi, H., Chen, X. & Riffat, S. 2018. Economic evaluation of installation of standalone wind farm and wind + CAES system for the new regulating tariffs for renewables in Egypt. Thermal Science and Engineering Progress 7(May): 311–325
  30. Razak, T.R., Garibaldi, J.M., Wagner, C., Pourabdollah, A. & Soria, D. 2021. Toward a Framework for Capturing Interpretability of Hierarchical Fuzzy Systems - A Participatory Design Approach. IEEE Transactions on Fuzzy Systems 29(5): 1160–1172. https://doi.org/10.1109/TFUZZ.2020.2969901
  31. Razak, T.R., Ismail, M.H., Fauzi, S.S.M., Gining, R.A.J. & Maskat, R. 2021. A framework to shape the recommender system features based on participatory design and artificial intelligence approaches. IAES International Journal of Artificial Intelligence 10(3): 727–734
  32. Sengupta, S., Sengupta, S., Chanda, C.K. & Saha, H. 2021. Modeling the Effect of Relative Humidity and Precipitation on Photovoltaic Dust Accumulation Processes. IEEE Journal of Photovoltaics 11(4): 1069–1077
  33. Tripathi, A.K., Aruna, M. & Murthy, C.S.N. 2017. Performance evaluation of PV panel under dusty condition. International Journal of Renewable Energy Development 6(3): 225–233. https://doi.org/10.14710/ijred.6.3.225-233
  34. Tripathi, A.K., Murthy, C.S.N. & Aruna, M. 2018. Experimental Investigation on the Influence of Dust on PV Panel Performance and Its Surface Temperature. Smart Systems and Green Energy 1(1): 15–22. https://doi.org/10.23977/ssge.2018.11002
  35. Younis, A. & Alhorr, Y. 2021. Modeling of dust soiling effects on solar photovoltaic performance : A review. Solar Energy 220(January): 1074–1088. https://doi.org/10.1016/j.solener.2021.04.011
  36. Zainuddin, H., Sallo, M.S., Shaari, S., Omar, A.M. & Sulaiman, S.I. 2015. Photovoltaic module temperature profile for Malaysia. 2015 IEEE Conference on Energy Conversion, CENCON 2015 469–473. https://doi.org/10.1109/CENCON.2015.7409590
  37. Zitouni, H., Azouzoute, A., Hajjaj, C., El Ydrissi, M., Regragui, M., Polo, J., Oufadel, A., Bouaichi, A. & Ghennioui, A. 2021. Experimental investigation and modeling of photovoltaic soiling loss as a function of environmental variables: A case study of semi-arid climate. Solar Energy Materials and Solar Cells 221(June 2020): 110874. https://doi.org/10.1016/j.solmat.2020.110874

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