تخمین میزان تابش خورشیدی در اقلیم‌های مختلف ایران با استفاده از روش‌های هیبریدی یادگیری ماشین

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه زابل، زابل،

2 گروه مهندسی آب، دانشکده انشکده اب و خاک، دانشگاه زابل، زابل

چکیده

تابش خورشیدی پارامتر اساسی مورد نیاز برنامه­های انرژی خورشیدی به­دلیل محدودیت­های تکنیکی و مالی در اقصی نقاط جهان اندازه­گیری نمی­شود و تعیین آن با روش­های تخمینی دقیق ضروری است. در این تحقیق، یک مدل برای تخمین تابش خورشیدی پراکنده­ی افقی روزانه با روش­های هیبریدی ماشین­بردار رگرسیونی، الگوریتم­های گرگ و هارمونی توسعه یافت. در بررسی اعتبار مدل، داده­های تابش خورشیدی اندازه­گیری­شده روزانه شهرهای مختلف با اقلیمهای متفاوت در قسمت آفتابی ایران(بندرعباس، کرمان، سنندج، سمنان و زاهدان) استفاده شد. پارامترهای ورودی مدل­ها، دمای متوسط، رطوبت نسبی، ساعت آفتابی، تبخیر و سرعت باد می­باشند. مقایسه تابش خورشیدی مدل هیبریدی با مقادیر اندازه­گیری­شده نشان­دهنده نتایج مطلوب بر اساس تحلیل آماری بوده و مدل ماشین بردار رگرسیونی - هارمونی روشی کارآمد و دقیق­ در مقایسه با سایر مدل­ها به­­ویژه مدل­های تجربی می­باشد. متوسط خطای بایاس مطلق بدست آمده، خطای مجذور متوسط ریشه و ضریب همبستگی برای ایستگاه زاهدان به ترتیب برابر با 77/14 مگاژول بر مترمربع، 85/ 26 مگاژول بر مترمربع و 68/0 برای معادله تجربی و مقادیر بدست آمده ماشین بردار رگرسیونی - هارمونی به ترتیب برابر با 85/13 مگاژول بر مترمربع ، 58/9 مگاژول بر مترمربع و 71/0 می­باشد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Estimation of solar radiation in different climates of Iran using hybrid machine learning methods

نویسندگان [English]

  • Nabi Jahantigh 1
  • jamshid Piri 2
1 Department of Mechanical Engineering, Faculty of Technology and Engineering, Zabol University, Zabol
2 Department of Water Engineering, Faculty Water and Soil, Zabol University, Zabol, Iran.
چکیده [English]

Solar radiation, the basic parameter required for solar energy programs, is not measured in all parts of the world due to technical and financial limitations, and it is necessary to determine with the accurate estimation methods. In this research, a model was developed to estimate the daily horizontal scattered solar radiation with the hybrid methods of Support Vector Regression, Gray Wolf Optimization and Harmony Search Algorithm. To determine the validity of the model, daily measured solar radiation data of different cities in the sunny part of Iran (Bandar Abbas, Kerman, Sanandaj, Semnan and Zahedan) were used. The input parameters of the models are the mean temperature, relative humidity, sunshine hour, evaporation and wind velocity. The comparison of the solar radiation of the hybrid model with the measured values shows the desired results based on statistical analysis, and the hybrid model is an efficient and accurate method compared to other models, especially experimental models. The average absolute bias error obtained, root mean square error and correlation coefficient for Zahedan station are respectively equal to 14.77 MJ⁄m^2, 26.85 MJ⁄m^2, and 0.68 for the experimental equation and the obtained values of the regression vector machine - Harmony respectively It was similar to 13.85MJ⁄m^2, 9.58 MJ⁄m^2 and 0.71. The research results showed that the regression-harmony vector machine model is an efficient method that is much more accurate than other models.

کلیدواژه‌ها [English]

  • Solar Radiation
  • climate change
  • Support Vector Regression
  • Wolf Optimization Algorithm
  • Harmony Search Algorithm
  • Experimental Models

مراجع

  1. Almorox, J., Bocco M., and Willington E., "Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina", Renewable Energy, 60: pp. 382-387, (2013).
  2. Okundamiya, M.S., Emagbetere J.O. and Ogujor E.A., "Evaluation of various global solar radiation models for Nigeria", International Journal of Green Energy, 13(5): pp. 505-512, (2016).
  3. Tang, K. Yang, J. He and J. Qin, "Quality control and estimation of global solar radiation in China", Solar Energy, 84(3): pp. 466-475, (2010).
  4. Şen, and A.D. Şahİn, "Spatial interpolation and estimation of solar irradiation by cumulative semivariograms", Solar Energy, 71(1): pp. 11-21, (2001).
  5. Piri and O. Kisi, "Modelling solar radiation reached to the Earth using ANFIS, NN-ARX, and empirical models (Case studies: Zahedan and Bojnurd stations)", Journal of Atmospheric and Solar-Terrestrial Physics, 123: pp. 39-47, (2015).
  6. R.S. Aldhshan, K.N. Abdul Maulud, W.S.W. Mohd Jaafar, O.A. Karim and B. Pradhan , "Energy consumption and spatial assessment of renewable energy penetration and building energy efficiency in Malaysia": A review. Sustainability, 13(16): pp. 9244, (2021).
  7. Kashyap, A. Bansal and A.K. Sao, "Solar radiation forecasting with multiple parameters neural networks", Renewable and Sustainable Energy Reviews, 49: pp. 825-835, (2015).
  8. Khorasanizadeh and K. Mohammadi, "Diffuse solar radiation on a horizontal surface: Reviewing and categorizing the empirical models", Renewable and Sustainable Energy Reviews, 53: pp. 338-362, (2016).
  9. S. Chukwujindu, "A comprehensive review of empirical models for estimating global solar radiation in Africa", Renewable and sustainable energy reviews, 78: pp. 955-995, (2017).
  10. Khosravi, R.N.N. Koury, L. Machado and J.J.G. Pabon, "Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms", Journal of Cleaner Production, 176: pp.63-75, (2018).
  11. Loutfi, A. Bernatchou and R. Tadili, "Generation of horizontal hourly global solar radiation from exogenous variables using an artificial neural network in Fes (Morocco)", International Journal of Renewable Energy Research, 3(7), pp. 1097-1107, (2017).
  12. A. Ibrahim and T. Khatib, "A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm", Energy Conversion and Management, 138: pp. 413-425, (2017).
  13. Belaid, A. Mellit, H. Boualit and M. Zayani , "Hourly global solar forecasting models based on a supervised machine learning algorithm and time series principle", International Journal of Ambient Energy, 43(1): pp. 1707-1718, (2022).
  14. Urraca, J. Antonanzas, M. Alia-Martinez, F.J. Martinez-de-Pison and F. Antonanzas-Torres, "Smart baseline models for solar irradiation forecasting", Energy conversion and management, 108: pp. 539-548, (2016).
  15. Voyant, G. Notton, S. Kalogirou , M-L. Nivet, C. Paoli, F. Motte and A. Fouilloy, "Machine learning methods for solar radiation forecasting", A review. Renewable Energy, 105: pp. 569-582, (2017).
  16. A. Abdalla, "New correlations of global solar radiation with meteorological parameters for Bahrain", International Journal of Solar Energy, 16(2): pp. 111-120, (1994)
  17. Y. Bae, H.S. Jang and D.K. Sung, "Hourly solar irradiance prediction based on support vector machine and its error analysis", IEEE Transactions on Power Systems, 32(2): pp. 935-945, (2016).
  18. Mirjalili, S.M. Mirjalili and A. Lewis, "Grey wolf optimizer", Advances in engineering software, 69: pp. 46-61, (2014).
  19. H. Sulaiman, Z. Mustaffa, M.R. Mohamed and O. Aliman, "Using the gray wolf optimizer for solving optimal reactive power dispatch problem", Applied Soft Computing, 32: p. 286-292, (2015).
  20. Pradhan, P.K. Roy and T. Pal, "Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system", Ain Shams Engineering Journal, 9(4): pp. 2015-2025, (2018).
  21. Moayedi, A.Osouli, D.T.Bui and L.K. Foong, "Spatial landslide susceptibility assessment based on novel neural-metaheuristic geographic information system based ensembles", Sensors, 19(21): pp. 4698, (2019).
  22. Dehghani, H. Riahi-Madvar, F. Hooshyaripor. A. Mosavi, S. Shamshirband, E.K. Zavadskas and K-W Chau, "Prediction of hydropower generation using grey wolf optimization adaptive neuro-fuzzy inference system", Energies, 12(2): pp. 289, (2019).
  23. Zhang and Y. Zhou, "Template matching using grey wolf optimizer with lateral inhibition", Optik, 130: pp. 1229-1243, (2017).
  24. Muro, R. Escobedo,L. Spector and R.P. Coppinger".,Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations", Behavioural processes, 88(3): pp. 192-197, (2011).
  25. W. Geem, Music-inspired harmony search algorithm: theory and applications, Vol. 191. Springer, 2009.
  26. S. Lee and Z.W. Geem, "A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice", Computer Methods in Applied Mechanics and Engineering, 194(36-38): pp. 3902-3933, (2005).
  27. W. Geem, "Novel derivative of harmony search algorithm for discrete design variables Environmental Planning and Management Program", Applied Mathematics and Computation, 199(1): pp. 223-230, (2008).
  28. W. Geem, C.-L. Tseng, and Y. Park "Harmony search for generalized orienteering problem: best touring in China", in International Conference on Natural Computation, Springer, (2005).
  29. M. Alia, M.A. Al-Betar, R. Mandava and A.T. Khader, "Data clustering using harmony search algorithm", in International Conference on Swarm, Evolutionary, and Memetic Computing, Springer, (2011).
  30. W. Geem, , "Harmony search for multiple dam scheduling, in Encyclopedia of artificial intelligence", IGI Global, pp. 803-807, (2009).
  31. A. Al-Betar and A.T. Khader, "A harmony search algorithm for university course timetabling", Annals of Operations Research, 194(1): pp. 3-31, (2012).
ارسال نظر در مورد این مقاله
CAPTCHA Image

فایل ها

هم رسانی

ارجاع به این مقاله

آمار