IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2024i1p174-d1555804.html
   My bibliography  Save this article

Performance Evaluation of Photovoltaic Panels in Extreme Environments: A Machine Learning Approach on Horseshoe Island, Antarctica

Author

Listed:
  • Mehmet Das

    (Department of Mechanical Engineering, Firat University, 23119 Elazig, Türkiye)

  • Erhan Arslan

    (Turkish Scientific and Research Council, Marmara Research Center, Polar Research Center (TÜBİTAK-MAM), 41470 Gebze, Türkiye)

  • Sule Kaya

    (Department of Software Engineering, Firat University, 23119 Elazig, Türkiye)

  • Bilal Alatas

    (Department of Software Engineering, Firat University, 23119 Elazig, Türkiye)

  • Ebru Akpinar

    (Department of Mechanical Engineering, Firat University, 23119 Elazig, Türkiye)

  • Burcu Özsoy

    (Turkish Scientific and Research Council, Marmara Research Center, Polar Research Center (TÜBİTAK-MAM), 41470 Gebze, Türkiye)

Abstract

Due to the supply problems of fossil-based energy sources, the tendency towards alternative energy sources is relatively high. For this reason, the use of solar energy systems is increasing today. This study combines experimental data and machine learning algorithms to evaluate the energy performance of four different photovoltaic (PV) panel designs (monocrystalline, polycrystalline, flexible, and transparent) under harsh environmental conditions on Horseshoe Island (Antarctica). In this research, the effects of environmental factors, such as solar radiation, temperature, humidity, and wind speed, on the panels were analyzed. Electrical power output of the PV panels are analyzed using six machine learning models. Random forest (RF) and CatBoost (CB) models showed the highest accuracy and reliability among these models. According to the experimental results, Monocrystalline PV provided the highest electrical power (20.5 Watts on average), and Flexible PV provided the highest energy efficiency (19.67%). However, Flexible PV was observed to have higher surface temperatures compared to the other panel types. Furthermore, using Monocrystalline PV resulted in an average reduction of 4.1 tons of CO 2 emissions per year, demonstrating the positive environmental impact of renewable energy systems. Thanks to this study, renewable energy research for temporary stations in Antarctica will focus on explainable and interpretable artificial intelligence models that will provide an understanding of the factors affecting the energy performance of PV panels. The research results will be an important guide for optimizing energy consumption, management, and demand forecasting in temporary research stations in Antarctica.

Suggested Citation

  • Mehmet Das & Erhan Arslan & Sule Kaya & Bilal Alatas & Ebru Akpinar & Burcu Özsoy, 2024. "Performance Evaluation of Photovoltaic Panels in Extreme Environments: A Machine Learning Approach on Horseshoe Island, Antarctica," Sustainability, MDPI, vol. 17(1), pages 1-34, December.
    • Handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:174-:d:1555804
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/1/174/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/1/174/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Baichao & Liu, Yanfeng & Wang, Dengjia & Song, Cong & Fu, Zhiguo & Zhang, Cong, 2024. "A review of the photothermal-photovoltaic energy supply system for building in solar energy enrichment zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    2. Francesco Nicoletti & Piero Bevilacqua, 2024. "Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support," Energies, MDPI, vol. 17(2), pages 1-22, January.
    3. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
    4. Ahmed Faris Amiri & Aissa Chouder & Houcine Oudira & Santiago Silvestre & Sofiane Kichou, 2024. "Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection," Energies, MDPI, vol. 17(13), pages 1-23, June.
    5. Wassila Tercha & Sid Ahmed Tadjer & Fathia Chekired & Laurent Canale, 2024. "Machine Learning-Based Forecasting of Temperature and Solar Irradiance for Photovoltaic Systems," Energies, MDPI, vol. 17(5), pages 1-20, February.
    6. Arslan, Erhan & Küçük, Furkan Ali & Biçer, Çetin & Özsoy, Burcu, 2024. "Determining energy, exergy and enviroeconomic analysis of stand-alone photovoltaic panel under harsh environment condition: Antarctica Horseshoe-Island cases," Renewable Energy, Elsevier, vol. 226(C).
    7. Ali Thakfan & Yasser Bin Salamah, 2024. "Artificial-Intelligence-Based Detection of Defects and Faults in Photovoltaic Systems: A Survey," Energies, MDPI, vol. 17(19), pages 1-23, September.
    8. Olivier, Jürgen R. & Harms, Thomas M. & Esterhuyse, Daniël J., 2008. "Technical and economic evaluation of the utilization of solar energy at South Africa's SANAE IV base in Antarctica," Renewable Energy, Elsevier, vol. 33(5), pages 1073-1084.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rômulo de Oliveira Azevêdo & Paulo Rotela Junior & Luiz Célio Souza Rocha & Gianfranco Chicco & Giancarlo Aquila & Rogério Santana Peruchi, 2020. "Identification and Analysis of Impact Factors on the Economic Feasibility of Photovoltaic Energy Investments," Sustainability, MDPI, vol. 12(17), pages 1-40, September.
    2. Liu, Mingzhe & Guo, Mingyue & Fu, Yangyang & O’Neill, Zheng & Gao, Yuan, 2024. "Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications," Applied Energy, Elsevier, vol. 372(C).
    3. Yanan Xue & Jinliang Yin & Xinhao Hou, 2024. "Short-Term Wind Power Prediction Based on Multi-Feature Domain Learning," Energies, MDPI, vol. 17(13), pages 1-25, July.
    4. Pei, Jingyin & Dong, Yunxuan & Guo, Pinghui & Wu, Thomas & Hu, Jianming, 2024. "A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting," Energy, Elsevier, vol. 305(C).
    5. Babinec, Susan & Baring-Gould, Ian & Bender, Amy N. & Blair, Nate & Li, Xiangkun & Muehleisen, Ralph T. & Olis, Dan & Ovaitt, Silvana, 2024. "Techno-economic analysis of renewable energy generation at the South Pole," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    6. Hong Wu & Haipeng Liu & Huaiping Jin & Yanping He, 2024. "Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data," Energies, MDPI, vol. 17(18), pages 1-19, September.
    7. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).
    8. Yiling Fan & Zhuang Ma & Wanwei Tang & Jing Liang & Pengfei Xu, 2024. "Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation," Energies, MDPI, vol. 17(14), pages 1-17, July.
    9. de Christo, Tiago Malavazi & Fardin, Jussara Farias & Simonetti, Domingos Sávio Lyrio & Encarnação, Lucas Frizera & de Alvarez, Cristina Engel, 2016. "Design and analysis of hybrid energy systems: The Brazilian Antarctic Station case," Renewable Energy, Elsevier, vol. 88(C), pages 236-246.
    10. Obydenkova, Svetlana V. & Pearce, Joshua M., 2016. "Technical viability of mobile solar photovoltaic systems for indigenous nomadic communities in northern latitudes," Renewable Energy, Elsevier, vol. 89(C), pages 253-267.
    11. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty," Applied Energy, Elsevier, vol. 371(C).
    12. Lakhdar Nadjib Boucetta & Youssouf Amrane & Aissa Chouder & Saliha Arezki & Sofiane Kichou, 2024. "Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model," Energies, MDPI, vol. 17(7), pages 1-21, April.
    13. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Self-learning dynamic graph neural network with self-attention based on historical data and future data for multi-task multivariate residential air conditioning forecasting," Applied Energy, Elsevier, vol. 364(C).
    14. Tin, Tina & Sovacool, Benjamin K. & Blake, David & Magill, Peter & El Naggar, Saad & Lidstrom, Sven & Ishizawa, Kenji & Berte, Johan, 2010. "Energy efficiency and renewable energy under extreme conditions: Case studies from Antarctica," Renewable Energy, Elsevier, vol. 35(8), pages 1715-1723.
    15. Ou, Yinlin & Hsiao, Cody Yu-Ling & Chui, Chin Man, 2024. "How does the supply chain market respond to policy shocks? Evidence from solar photovoltaic sectors in China," Renewable Energy, Elsevier, vol. 232(C).
    16. Yu, Hanxin & Chen, Shanlin & Chu, Yinghao & Li, Mengying & Ding, Yueming & Cui, Rongxi & Zhao, Xin, 2024. "Self-attention mechanism to enhance the generalizability of data-driven time-series prediction: A case study of intra-hour power forecasting of urban distributed photovoltaic systems," Applied Energy, Elsevier, vol. 374(C).
    17. Zhou, Xinping & Yang, Jiakuan & Wang, Fen & Xiao, Bo, 2009. "Economic analysis of power generation from floating solar chimney power plant," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 736-749, May.
    18. Afshar, O. & Saidur, R. & Hasanuzzaman, M. & Jameel, M., 2012. "A review of thermodynamics and heat transfer in solar refrigeration system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5639-5648.
    19. Aissa Meflah & Fathia Chekired & Nadia Drir & Laurent Canale, 2024. "Accurate Method for Solar Power Generation Estimation for Different PV (Photovoltaic Panels) Technologies," Resources, MDPI, vol. 13(12), pages 1-18, November.
    20. Okoye, Chiemeka Onyeka & Oranekwu-Okoye, Blessing Chioma, 2018. "Economic feasibility of solar PV system for rural electrification in Sub-Sahara Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2537-2547.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2024:i:1:p:174-:d:1555804. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.