Open Access Open Access  Restricted Access Subscription or Fee Access

Forecasting the Solar Panels Power Output Based on Air Pollution and Weather in the Gulf Countries by Using Machine Learning


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iree.v17i6.22714

Abstract


Solar panels' power output is greatly impacted by environmental factors such as weather patterns and air pollution. A solar panel's ability to generate power is limited by unfavorable weather conditions and pollution in the air. To ensure that solar panels are installed correctly and perform optimally, it is particularly important to understand their power output prior to their installation. A method for predicting solar panel power output is presented in this paper based on weather and air pollution characteristics. In order to develop machine learning models, three types of features are taken into account, including the weather, air pollution, and the combination of the two. The data was collected from the Gulf Countries (Saudi Arabia, the United Arab Emirates, Kuwait, Bahrain, Qatar and Oman) between 2018 and 2020. Experiments conducted on solar panels indicate that weather and air pollution can be decisive factors in predicting power output.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords


Machine Learning; Solar Panel Power; Air Pollution; Weather and Solar Panel

Full Text:

PDF


References


A. Sayyah, M.N. Horenstein, & M.K. Mazumder (2014). Energy yield loss caused by dust deposition on photovoltaic panels. Solar Energy, 107, 576-604.
https://doi.org/10.1016/j.solener.2014.05.030

A. Ennaoui , B. Figgis, & D.M. Plaza (2016, March). Outdoor Testing in Qatar of PV Performance, Reliability and Safety. In Qatar Foundation Annual Research Conference Proceedings Volume 2016 Issue 1 (Vol. 2016, No. 1, p. EEPP2538). Hamad bin Khalifa University Press (HBKU Press).
https://doi.org/10.5339/qfarc.2016.EEPP2538

Hawashin, D., Alkhateri, M., Alnuaimi, N., Saif, F., Omer, Z., Shareef, H., Performance Evaluation of Recent Metaheuristic Optimization Algorithms for Photovoltaic System Parameter Extraction, (2021) International Review of Electrical Engineering (IREE), 16 (1), pp. 60-67.
https://doi.org/10.15866/iree.v16i1.18955

A.F. Borges, F.J. Laurindo, M.M. Spínola , R.F. Gonçalves, & C.A. Mattos (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225.
https://doi.org/10.1016/j.ijinfomgt.2020.102225

Y. Sun, G. Szűcs, & A.R. Brandt (2018). Solar PV output prediction from video streams using convolutional neural networks. Energy & Environmental Science, 11(7), 1811-1818.
https://doi.org/10.1039/C7EE03420B

A. Dolara , S. Leva & G. Manzolini. (2015). Comparison of different physical models for PV power output prediction. Solar energy, 119, 83-99.
https://doi.org/10.1016/j.solener.2015.06.017

Martínez-Peñaloza, A., Osma-Pinto, G., Analysis of the Performance of the Norton Equivalent Model of a Photovoltaic System Under Different Operating Scenarios, (2021) International Review of Electrical Engineering (IREE), 16 (4), pp. 328-343.
https://doi.org/10.15866/iree.v16i4.20278

W. VanDeventer , E. Jamei, G. S. Thirunavukkarasu, M. Seyedmahmoudian, T. K. Soon, B. Horan, & A. Stojcevski (2019). Short-term PV power forecasting using hybrid GASVM technique. Renewable energy, 140, 367-379.
https://doi.org/10.1016/j.renene.2019.02.087

A. Khandakar, M. EH Chowdhury, M. Khoda Kazi, K. Benhmed, F. Touati , M. Al-Hitmi & J.S. Gonzales(2019). Machine learning based photovoltaics (PV) power prediction using different environmental parameters of Qatar. Energies, 12(14), 2782.
https://doi.org/10.3390/en12142782

N. Ahmad, A. Khandakar, A. El-Tayeb, K. Benhmed, A. Iqbal, & F. Touati. Novel Design for Thermal Management of PV Cells in Harsh Environmental Conditions. Energies 2018, 11, 3231.
https://doi.org/10.3390/en11113231

Buitrago, M., Candelo-Becerra, J., Bolaños Martinez, F., Optimal Energy Management System Applied to a Building Involving Solar Renewable Systems, Electrical Vehicles, and Storage Systems, (2022) International Review of Electrical Engineering (IREE), 17 (3), pp. 316-326.doi: https://doi.org/10.15866/iree.v17i3.20070
https://doi.org/10.15866/iree.v17i3.20070

Qasim, M., Velkin, V., Maximum Power Point Tracking Techniques for Micro-Grid Hybrid Wind and Solar Energy Systems - a Review, (2020) International Journal on Energy Conversion (IRECON), 8 (6), pp. 223-234.
https://doi.org/10.15866/irecon.v8i6.19502

Rerhrhaye, F., Lahlouh, I., Ennaciri, Y., Benzazah, C., Akkary, A., Sefiani, N., New Solar MPPT Control Technique Based on Incremental Conductance and Multi-Objective Genetic Algorithm Optimization, (2022) International Journal on Energy Conversion (IRECON), 10 (3), pp. 70-78.
https://doi.org/10.15866/irecon.v10i3.22156

Zhalnin, V., Zakharova, A., Uzenkov, D., Vlasov, A., Krivoshein, A., Filin, S., Configuration-Making Algorithm for the Smart Machine Controller Based on the Internet of Things Concept, (2019) International Review of Electrical Engineering (IREE), 14 (5), pp. 375-384.
https://doi.org/10.15866/iree.v14i5.16923

A. Moosa, H. Shabir, H. Ali, R. Darwade, & B. Gite. Predicting Solar Radiation Using Machine Learning Techniques. In Proceedings of the 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 14-15 June 2018.
https://doi.org/10.1109/ICCONS.2018.8663110

F. Jawaid, & K. NazirJunejo. Predicting daily mean solar power using machine learning regression techniques In Proceedings of the 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland, 24-26 August 2016.
https://doi.org/10.1109/INTECH.2016.7845051

S. Netsanet, J. Zhang, D. Zheng, R.K Agrawal, & F. Muchahary. An aggregative machine learning approach for output power prediction of wind turbines. In Proceedings of the 2018 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA, 8-9 February 2018.
https://doi.org/10.1109/TPEC.2018.8312085

S. Mishra, & P. Dash. Short term wind power forecasting using Chebyshev polynomial trained by ridge extreme learning machine. In Proceedings of the 2015 IEEE Power, Communication and Information Technology Conference (PCITC), Bhubaneswar, Odisha, India, 15-17 October 2015.
https://doi.org/10.1109/PCITC.2015.7438155

F. Touati, N.A. Chowdhury, K. Benhmed, A.J.S.P. Gonzales, M.A. Al-Hitmi, M. Benammar, A. Gastli, & L. Ben-Brahim. Long-term performance analysis and power prediction of PV technology in the State of Qatar, Renew. Energy 2017, 113, 952-965.
https://doi.org/10.1016/j.renene.2017.06.078

Z.A. Darwish, H.A. Kazem, K. Sopian, M.A. Alghoul, & H. Alawadhi. Experimental investigation of dust pollutants and the impact of environmental parameters on PV performance: An experimental study. Environ.Dev. Sustain. 2018, 20, 155-174.
https://doi.org/10.1007/s10668-016-9875-7

M. Benghanem, A. Almohammedi, M.T. Khan, & A. Al-Masraqi, Effect of dust accumulation on the performance of photovoltaic panels in desert countries: A case study for Madinah, Saudi Arabia. Int. J.Power Electron. Drive Syst. 2018, 9, 1356-1366.
https://doi.org/10.11591/ijpeds.v9.i3.pp1356-1366

Y.N. Chanchangi, A. Ghosh , S. Sundaram & T.K. Mallick (2020) Dust and PV performance in Nigeria: a review. Renew Sustain Energy Rev 121:109704.
https://doi.org/10.1016/j.rser.2020.109704

R. Shenouda , M.S. Abd-Elhady, & H.A. Kandil. A review of dust accumulation on PV panels in the MENA and the Far East regions. J. Eng. Appl. Sci. 69, 8 (2022).
https://doi.org/10.1186/s44147-021-00052-6

German Osma-Pinto & Gabriel Ordóñez-Plata, Measuring factors influencing performance of rooftop PV panels in warm tropical climates, Solar Energy, Volume 185, 2019, Pages 112-123, ISSN 0038-092X.
https://doi.org/10.1016/j.solener.2019.04.053

Wiktoria Grycan, Bartosz Brusilowicz & Marek Kupaj. Photovoltaic farm impact on parameters of power quality and the current legislation, Solar Energy, Volume 165,2018,Pages 189-198, ISSN 0038-092X.
https://doi.org/10.1016/j.solener.2018.03.016

Prakash, S., Dhal, P., A Review: Solar Tracking System with Grid Used in Kurnool Ultra Mega Solar Park, (2019) International Review of Electrical Engineering (IREE), 14 (3), pp. 195-204.
https://doi.org/10.15866/iree.v14i3.17162

Sanaz Ghazi, Ali Sayigh & Kenneth Ip , Dust effect on flat surfaces - A review paper, Renewable and Sustainable Energy Reviews, Volume 33,2014, Pages742-751, ISSN 1364-0321.
https://doi.org/10.1016/j.rser.2014.02.016

Mariam Al Kandari & Imtiaz Ahmad, Solar power generation forecasting using ensemble approach based on deep learning and statistical methods, Applied Computing and Informatics (Nov 2019), ISSN: 2634-1964.
https://doi.org/10.1016/j.aci.2019.11.002

S. Sobri, S. Koohi-Kamali, N.A. Rahim, Solar photovoltaic generation forecasting methods: a review, Energy Convers. Manage. 156 (2018) pp. 459-497.
https://doi.org/10.1016/j.enconman.2017.11.019

S. Makridakis, E. Spiliotis & V. Assimakopoulos, Statistical and machine learning forecasting methods: Concerns and ways forward, PloS One 13 (3) (2018) e0194889.
https://doi.org/10.1371/journal.pone.0194889

D. Yang & Z. Dong, Operational photovoltaics power forecasting using seasonal time series ensemble, Sol. Energy 166 (2018) pp. 529-541.
https://doi.org/10.1016/j.solener.2018.02.011

www.kaggle.com/datasets/MKA_GCC_Power

Mnguni, M., Tzoneva, R., Development and Real-Time Implementation of an Under Voltage Load Shedding Scheme Using a Real-Time Digital Simulator, (2019) International Review of Electrical Engineering (IREE), 14 (6), pp. 420-437.
https://doi.org/10.15866/iree.v14i6.16609

Moreno-Chuquen, R., Florez-Cediel, O., Online Dynamic Assessment of System Stability in Power Systems Using the Unscented Kalman Filter, (2019) International Review of Electrical Engineering (IREE), 14 (6), pp. 465-472.
https://doi.org/10.15866/iree.v14i6.16979


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize