مطالعۀ مروری مدل‌سازی پارامترهای محیطی مؤثر بر عملکرد سامانه‌های فتوولتاییک خورشیدی

نوع مقاله : مروری

نویسندگان

1 دانشجوی دکتری، گروه مهندسی انرژی‌های تجدیدپذیر، دانشگاه شهید بهشتی، تهران

2 استادیار، گروه مهندسی انرژی‌های تجدیدپذیر، دانشگاه شهید بهشتی، تهران

3 دانشیار، گروه مهندسی انرژی‌های تجدیدپذیر، دانشگاه شهید بهشتی، تهران

چکیده

هدف اصلی مطالعۀ پیش رو، مرور نظام‌مند پژوهش‌های انجام‌شده در زمینۀ مدل‌سازی پارامترهای محیطی مؤثر بر سامانه‌های فتوولتاییک و بررسی روندهای نوین روش‌شناسی در این حوزه است. عملکرد سامانه‌های فتوولتاییک به‌شدت تحت تأثیر عواملی مانند تابش خورشید، دما، گردوغبار، سرعت باد، رطوبت، بارش و فشار هوا قرار دارد. مدل‌سازی دقیق این عوامل برای افزایش بهره‌وری و قابلیت اطمینان سامانه‌های فتوولتاییک ضروری است. در این راستا، پژوهش حاضر با بهره‌گیری از تحلیل مرجع‌شناسی و تحلیل محتوایی، مقالات نمایه‌شده در پایگاه Scopus طی سال‌های 1984 تا 2024 را بررسی کرده است. نتایج نشان می‌دهد این حوزه با نرخ رشد سالانه 13/9 درصد توسعه یافته و کشورهای چین، هند، ایتالیا، آمریکا و ایران سهم عمده‌ای در تولیدات علمی داشته‌اند. رویکردهای مدل‌سازی در سه دسته جعبه‌سفید، جعبه‌سیاه و ترکیبی طبقه‌بندی شدند. یافته‌ها نشان می‌دهد در حالی که مدل‌های جعبه‌سفید تفسیرپذیری بالایی دارند، مدل‌های جعبه‌سیاه مبتنی بر یادگیری ماشین دقت پیش‌بینی بیشتری دارند. مدل‌های ترکیبی که تلفیقی از هر دو رویکرد هستند، دقیق‌ترین نتایج را ارائه می‌دهند. این مطالعه ضمن تأکید بر نقش رو‌به‌رشد یادگیری ماشین در بهبود عملکرد سامانه‌های فتوولتاییک، مسیرهای پژوهشی آینده شامل توسعۀ مدل‌های ترکیبی، استفاده از داده‌های لحظه‌ای مبتنی بر اینترنت اشیا و بهره‌گیری از هوش مصنوعی تفسیرپذیر را پیشنهاد می‌کند.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Review of Environmental Parameter Modeling Affecting the Performance of Solar Photovoltaic Systems

نویسندگان [English]

  • Mahdi Gandomzadeh 1
  • Aslan Gholami 2
  • Majid Zandi 3
1 Department of Renewable Energy Engineering, Shahid Beheshti University, Tehran, Iran
2 Department of Renewable Energy Engineering, Shahid Beheshti University, Tehran, Iran
3 Department of Renewable Energy Engineering, Shahid Beheshti University, Tehran, Iran
چکیده [English]

The main objective of this study is to systematically review research on environmental parameter modeling for photovoltaic systems and to highlight recent methodological trends. PV performance is strongly influenced by environmental factors such as solar irradiance, temperature, wind speed, dust, humidity, precipitation, and atmospheric pressure. Accurate modeling of these parameters is essential for improving efficiency and reliability. To address this, the study employs bibliometric and content analysis of Scopus publications from 1984 to 2024. Results show a 9.13% annual growth in related studies, with China, India, Italy, the U.S., and Iran as leading contributors. Modeling approaches are categorized into white-box, black-box, and hybrid methods. Findings indicate that while white-box models provide interpretability, machine learning-based black-box models achieve higher predictive accuracy. Hybrid models, integrating physical and data-driven techniques, offer the most robust solutions. The study underscores the increasing role of ML in PV performance and recommends future research on hybrid frameworks, IoT-enabled data collection, and explainable AI.

کلیدواژه‌ها [English]

  • Solar photovoltaic systems
  • Environmental parameter modeling
  • Machine learning
  • Bibliometric analysis
  • Content analysis

مراجع

  • Mahmoudian Yonesi S, Gandomzadeh M, Mosayyebi A, Mazlom Farsibaf M, Zandi M. Modeling the supply chain of LNG with system dynamic method (Case study: Iran). Strateg Stud Pet Energy Ind. 2024;60(16).
  • Jami M, Parsay A, Gandomzadeh M, Mosayyebi A, Gavagsaz-Ghoachani R, Zandi M. Future Energy Modeling Pathway in Iran: Scenario Analysis Using Cross-Impact Balance Method. J Sustain Energy Syst. 2024;3(2):193–208.
  • Gholami A, Deghan Banadaky M, Gholami A. Advancing Sustainability in Dairy Farming: Integrating Renewable Energy and Dietary Innovations. In: 2025 10th International Conference on Technology and Energy Management (ICTEM). 2025.
  • Parsay A, Gandomzadeh M, Gholami A, Gavagsaz-Ghoachani R, Phattanasak M. Multi-Purposeful Visualization Card: A Creative Teaching Tool For Attractive Engineering Education. 4th Res Invent Innov Congr Innov Better Life (RI2C 2023). 2023;1–4.
  • Parsay A, Gandomzadeh M, Yaghoubi AA, Hoorsun A, Gholami A, Zandi M, et al. Enhancing photovoltaic efficiency: An in-depth systematic review and critical analysis of dust monitoring, mitigation, and cleaning techniques. Appl Energy [Internet]. 2025 Jun;388:125668. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0306261925003988
  • Alvari Y, Zandi M, Jahangiri A, Ameri M, Gholami A, Shahidi P, et al. BIPV-driven smart vertical greenhouses: a water energy food environment nexus framework for sustainable urban agriculture. Energy Nexus. 2025;19.
  • Akhbari H, Eslami S, Gholami A. Long-term performance evaluation of hybrid solar thermal and desalination Systems: Heat transfer fluids and advanced configurations insights. Case Stud Therm Eng. 2025;73:106601.
  • Goharian MS, Gandomzadeh M, Gholami A, Gavagsaz-Ghoachani R, Phattanasak M. Floating Solar Photovoltaics: Design Classifications and Technological Trends. Proc - 2025 IEEE 7th Glob Power, Energy Commun Conf GPECOM 2025. 2025;668–72.
  • Minoofar A, Zandi A, Gholami A. Precise prediction models for floating and land-based Photovoltaics: Comparative analysis for advancing sustainable energy and water management solutions. Renew Energy. 2025;255.
  • Piryaei Z, Gholami A, Zandi M, Gavagsaz-Ghoachani R, Phattanasak M. Advances in Nanotechnology for Sustainable Photocatalytic Water Splitting. In: V International Conference on Electrical, Computer and Energy Technologies (ICECET 2025). Paris: IEEE; 2025.
  • Yousefi Roshan A, Gandomzadeh M, Alvari Y, Gholami A, Zandi M. Nanomaterials in Zero Energy Buildings: A Comprehensive Review of Achievements, Challenges, and Urban Perspectives. Urban Dev Policy Mak. 2025;
  • Yaghoubi AA, Gandomzadeh M, Gholami A, Gavagsaz-Ghoachani R, Zandi M, Phattanasak M. Harnessing Machine Learning with Advanced Linear Regression Models to Forecast PV System. In: 2024 International Conference on Materials and Energy: Energy in Electrical Engineering (ICOME 2024) [Internet]. IEEE; 2024. p. 1–4. Available from: https://ieeexplore.ieee.org/document/10845673/
  • Gandomzadeh M, Kabiri-Sedeh M, Gavagsaz-Ghoachani R, Zandi M. Practical reliability study of a photovoltaic system for the supply of home load profile; Case study: Tehran city. In: 2019 Iranian Conference on Renewable Energy & Distributed Generation (ICREDG2019). Tehran; 2019. p. 1–6.
  • Hoorsun A, Gandomzadeh M, Yaghoubi AA, Parsay A, Gholami A, Zandi M. Insights and Research Trends of Dust and Cleaning in Solar Energy: A Bibliometric Review Study. In: 2024 9th International Conference on Technology and Energy Management, ICTEM 2024. Behshahr, Mazandaran, Iran: IEEE; 2024. p. 1–5.
  • Bozorgpuore S, Gholami A. Impacts of Shading on Photovoltaic Panel Performance and Mitigating Strategies. Iran Electr Ind J Qual Product. 2025;14(2):1–13.
  • Ameri M, Gandomzadeh M, Yaghoubi AA, Gholami A, Zandi M, Gavagsaz-Ghoachani R. Revolutionizing Solar Panel Maintenance in Photovoltaic Systems: Reviewing Intelligent UAV Solutions for Efficient Dust Mitigation and Perspectives. In: 7th International Conference on Advances in Mechanical Engineering (ICAME 2024). 2024. p. 111–9.
  • Gandomzadeh M, Gholami A, Zandi M. Modeling Environmental Parameters Affecting the Performance of Solar Photovoltaic Systems Using Machine Learning. 2025 10th Int Conf Technol Energy Manag ICTEM 2025. 2025;
  • Rostami S, Zeinalpourcharigh H, Kouhzadi Hikoee MA, Gandomzadeh M, Minoofar A, Gholami A, et al. Convolutional Neural Networks for Hotspot Detection in Photovoltaic Systems. In: V International Conference on Electrical, Computer and Energy Technologies (ICECET 2025). Paris: IEEE; 2025.
  • Kouhzadi Hikoee MA, Rostami S, Zeinalpourcharigh H, Gandomzadeh M, Minoofar A, Gholami A, et al. PV Panel Defect Detection Based on CNN Machine Learning Using Electroluminescence Dataset. In: V International Conference on Electrical, Computer and Energy Technologies (ICECET 2025). Paris: IEEE; 2025.
  • Gholami K, Karimi S, Rastgou A. Fuzzy risk-based framework for scheduling of energy storage systems in photovoltaic-rich networks. J Energy Storage. 2022;52.
  • Piryaei Z, Gholami A, Zandi M. 4E performance evaluation of renewable microgrids : Comparing hydrogen and battery storage for nearly net zero energy buildings. Energy Convers Manag [Internet]. 2025;332(March):119711. Available from: https://doi.org/10.1016/j.enconman.2025.119711
  • Gandomzadeh M, Malandish E, Gavagsaz-Ghoachani R, Jafari H, Phattanasak M. Purposeful Learning Booklet a method for teaching Engineering. In: 2019 6th International Conference on Technical Education (ICTechEd6). 2019. p. 1–6.
  • Yaghoubi AA, Gandomzadeh M, Gholami A, Gavagsaz Ghoachani R, Zandi M, A. Kazem H. Optimize photovoltaic panels cleaning scheduling framework based on variations of hourly-based active electricity pricing in the market. Sol Energy. 2024 Jun;275(May):112633.
  • Gholami A, Ameri M, Zandi M, Ghoachani RG, Kazem HA. Predicting solar photovoltaic electrical output under variable environmental conditions: Modified semi-empirical correlations for dust. Energy Sustain Dev. 2022;71:389–405.
  • Gandomzadeh M, Gholami A, Zandi M. Modeling Environmental Parameters Affecting the Performance of Solar Photovoltaic Systems Using Machine Learning. In: 2025 10th International Conference on Technology and Energy Management (ICTEM2025). Tabriz; 2025.
  • Gandomzadeh M, Mahmoudian Younesi S, Mosayyebi A, Zandi M. Development scenarios for electrical energy storage in Iran with Cross-Impact Balance method. J Sustain Energy Syst [Internet]. 2022;1(4):373–96. Available from: https://ses.ut.ac.ir/article_91815.html?lang=en
  • Gandomzadeh M, Yaghoubi AA, Hoorsun A, Parsay A, Gholami A, Zandi M, et al. Dust mitigation methods and multi-criteria decision-making cleaning strategies for photovoltaic systems: Advances, challenges, and future directions. Energy Strateg Rev. 2025;57:101629.
  • Rezvani M, Gholami A, Gavagsaz-Ghoachani R, Zandi M. A Review on The Effect of Dust Properties on Photovoltaic Solar Panels’ Performance. J Renew New Energy. 2023 Mar;10(1):198–211.
  • Yaghoubi AA, Gandomzadeh M, Parsay A, Gholami A, Gavagsaz-Ghoachani R, Zandi M. A Review on Machine Learning Model Implementation for Photovoltaic Systems. In: 2024 11th Iranian Conference on Renewable Energy and Distribution Generation, ICREDG 2024. Yazd, Iran: IEEE; 2024. p. 1–5.
  • Jordehi AR. Parameter estimation of solar photovoltaic (PV) cells: A review. Renew Sustain Energy Rev. 2016;61:354–71.
  • Chen D, Irwin D. Black-box solar performance modeling: Comparing physical, machine learning, and hybrid approaches. ACM SIGMETRICS Perform Eval Rev. 2017;45(2):79–84.
  • Gaviria JF, Narváez G, Guillen C, Giraldo LF, Bressan M. Machine learning in photovoltaic systems: A review. Renew Energy. 2022;196:298–318.
  • Gholami A, Ameri M, Zandi M, Gavagsaz Ghoachani R. Electrical, thermal and optical modeling of photovoltaic systems: Step-by-step guide and comparative review study. Sustain Energy Technol Assessments. 2022 Feb;49:101711.
  • Silva LAC, Ruiz LGB, Criado-Ramón D, Bessa JG, Micheli L, Jiménez M del CP. Assessing the impact of soiling on photovoltaic efficiency using supervised learning techniques. Expert Syst Appl. 2023;231:120816.
  • Yaghoubi AA, Gandomzadeh M, Gholami A, Gavagsaz-Ghoachani R, Zandi M. Long-term comparative analysis of machine learning models: A deep dive into applications of artificial intelligence for enhancing photovoltaic performance prediction. Int J Electr Power Energy Syst. 2025;170.
  • Gholami A, Ameri M, Zandi M, Gavagsaz Ghoachani R. Electrical, thermal and optical modeling of photovoltaic systems: Step-by-step guide and comparative review study. Sustain Energy Technol Assessments. 2022;49.
  • Zandi M, Amirhosseini M. Solar Energy; Solar Photovoltaic Systems. 2021. 418 p.
  • Baig MQ, Khan HA, Ahsan SM. Evaluation of solar module equivalent models under real operating conditions - A review. J Renew Sustain Energy. 2020 Jan;12(1):012701.
  • Yıldıran N, Tacer E. Identification of photovoltaic cell single diode discrete model parameters based on datasheet values. Sol Energy. 2016;127:175–83.
  • Notton G, Cristofari C, Mattei M, Poggi P. Modelling of a double-glass photovoltaic module using finite differences. Appl Therm Eng. 2005;25(17–18):2854–77.
  • Siddiqui MU, Arif AFM, Kelley L, Dubowsky S. Three-dimensional thermal modeling of a photovoltaic module under varying conditions. Sol energy. 2012;86(9):2620–31.
  • Weiss L, Amara M, Ménézo C. Impact of radiative-heat transfer on photovoltaic module temperature. Prog Photovoltaics Res Appl. 2016 Jan;24(1):12–27.
  • Smestad GP, Germer TA, Alrashidi H, Fernández EF, Dey S, Brahma H, et al. Modelling photovoltaic soiling losses through optical characterization. Sci Rep. 2020;10(1):58.
  • Russell SJ, Norvig P. Artificial intelligence: a modern approach. Pearson; 2016.
  • Sahin G, Isik G, van Sark WGJHM. Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression. Energy Reports. 2023;10:2837–49.
  • Paletta Q, Hu A, Arbod G, Lasenby J. ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy. Appl Energy. 2022;326.
  • Jiao X, Li X, Yang Y, Xiao W. Novel and comprehensive approach for power loss estimation of soiled photovoltaic modules. Sol Energy. 2024;268:112283.
  • Kassem Y, Othman AA. Selection of most relevant input parameters for predicting photovoltaic output power using machine learning and quadratic models. Model Earth Syst Environ. 2022;8(4):4661–86.
  • Tina GM, Ventura C, Ferlito S, De Vito S. A state-of-art-review on machine-learning based methods for PV. Vol. 11, Applied Sciences (Switzerland). 2021.
  • Keddouda A, Ihaddadene R, Boukhari A, Atia A, Arıcı M, Lebbihiat N, et al. Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation. Appl Energy. 2024;363:123064.
  • Kayri I, Aydin H. ANN Based Prediction of Module Temperature in a Single Axis PV System. In: IEEE Global Energy Conference, GEC 2022. 2022. p. 361–7.
  • Fara L, Diaconu A, Craciunescu D, Fara S. Forecasting of Energy Production for Photovoltaic Systems Based on ARIMA and ANN Advanced Models. Int J Photoenergy. 2021;2021.
  • Li J, Ward JK, Tong J, Collins L, Platt G. Machine learning for solar irradiance forecasting of photovoltaic system. Renew Energy. 2016;90:542–53.
  • Ceylan I, Erkaymaz O, Gedik E, Gurel AE. The prediction of photovoltaic module temperature with artificial neural networks. Case Stud Therm Eng. 2014;3:11–20.
  • Hammad B, Al-Abed M, Al-Ghandoor A, Al-Sardeah A, Al-Bashir A. Modeling and analysis of dust and temperature effects on photovoltaic systems’ performance and optimal cleaning frequency: Jordan case study. Renew Sustain Energy Rev. 2018 Feb;82(April 2017):2218–34.
  • Sharma S, Joshua Thomas J, Vasant P. Performance Analysis and Effects of Dust & Temperature on Solar PV Module System by Using Multivariate Linear Regression Model. In: Artificial Intelligence for Renewable Energy and Climate Change. 2022. p. 217–75.
  • Amer HN, Dahlan NY, Azmi AM, Latip MFA, Onn MS, Tumian A. Solar power prediction based on Artificial Neural Network guided by feature selection for Large-scale Solar Photovoltaic Plant. Energy Reports. 2023;9:262–6.
  • Durganjali CS, Avinash G, Megha K, Ponnalagu RN, Goel S, Radhika S. Prediction of PV cell parameters at different temperatures via ML algorithms and comparative performance analysis in Multiphysics environment. Energy Convers Manag. 2023;282:116881.
  • Mohammad AT, Hussen HM, Akeiber HJ. Prediction of the output power of photovoltaic module using artificial neural networks model with optimizing the neurons number. Int J Renew Energy Dev. 2023 May;12(3):478–87.
  • Villan JS, Mantalaba RR, Diaz RJB, Albios KDO, Elevencionado VT, Benares NP, et al. Dust Impact Assessment on Micro Solar Voltage: A Deep Learning-Enabled Prediction Approach. In: 2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM). 2024. p. 1–5.
  • Yousif JH, Kazem HA, Bwalya KJ. Dust impact on photovoltaic technologies: a comparative analysis using deep recurrent neural networks. Energy Sources, Part A Recover Util Environ Eff. 2024;46(1):3023–40.
  • Tossa AK, Soro YM, Coulibaly Y, Azoumah Y, Migan-Dubois A, Thiaw L, et al. Artificial intelligence technique for estimating PV modules performance ratio under outdoor operating conditions. J Renew Sustain Energy. 2018;10(5).
  • Tripathi AK, Aruna M, Venkatesan EP, Abbas M, Afzal A, Shaik S, et al. Quantitative Analysis of Solar Photovoltaic Panel Performance with Size-Varied Dust Pollutants Deposition Using Different Machine Learning Approaches. Molecules. 2022;27(22).
  • Ra N, Varman S, Antony Joseph K, Bhattacharjee A. Prediction of Optical Performance of Solar PV under the Impact of Natural Dust Accumulation: Machine Learning Approach. In: 2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies, GlobConHT 2023. 2023. p. 1–5.
  • Kayri İ, Bayar MT. A new approach to determine the long-term effect of efficiency losses due to different dust types accumulation on PV modules with artificial neural networks. J Clean Prod. 2024;434:140282.
فصلنامه سیستم های انرژی پایدار
دوره 5، شماره 1
دی 1404
صفحه 165-185

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