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Multi-region Modeling of Daily Global Solar Radiation with Artificial Intelligence Ensemble

  • Vahid NouraniEmail author
  • Gozen Elkiran
  • Jazuli Abdullahi
  • Ala Tahsin
Original Paper
  • 63 Downloads

Abstract

Solar radiation data are crucial for the design and evaluation of solar energy systems, climatic studies, water resources management, estimating crop productivity, etc. As so, for locations where direct measurements are not available, reliable models may be developed to estimate solar radiation from more readily available data. In this study, two artificial intelligence (AI)-based models including artificial neural network and adaptive neuro-fuzzy inference systems, three temperature-based empirical models including Meza–Varas, Hargreaves–Samani, and Chen, and a conventional multi-linear regression (MLR) model were employed for multi-region daily global solar radiation estimation for Iraq. To ensure appropriate selection of input variables, sensitivity analysis was conducted to determine the dominant parameters. Finally, two ensemble approaches, neural average ensemble and simple average ensemble, were applied to improve the performance of the single models. For this purpose, daily meteorological data of maximum temperature \( \left({T_{\hbox{max} } } \right) \), minimum temperature \( \left({T_{\hbox{min} } } \right) \), mean temperature \( \left({T_{\text{mean}} } \right) \), relative humidity \( \left({R_{\text{H}} } \right) \), and wind speed \( \left({U_{2} } \right) \) were obtained from January 2006 to December 2016 from four major cities in Iraq representing, north, west, south, and east regions. The results revealed that temperatures \( \left({T_{\hbox{max} } , \;T_{\text{mean}} ,\; T_{\hbox{min} } } \right) \) and relative humidity are the dominant parameters. While temperature-based empirical models and MLR model could be employed to achieve reliable results, AI-based models are superior in performance to other models. Also promising improvement in daily global solar radiation modeling could be achieved by model ensemble. The results of this study affirmed that the provided ensemble approaches can increase the performance of single models up to 19.19%, 7.59%, and 16.81% in training, validation, and testing steps, respectively.

Keywords

Artificial intelligence Empirical models Solar radiation estimation Ensemble approaches Iraq 

References

  1. Al-Alawi, S. M., & Al-Hinai, H. A. (1998). An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation. Renewable Energy, 14(1–4), 199–204.Google Scholar
  2. Almorox, J., Hontoria, C., & Benito, M. (2011). Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain). Applied Energy, 88(5), 1703–1709.Google Scholar
  3. Aqil, M., Kita, I., Yano, A., & Nishiyama, S. (2007). Analysis & prediction of flow from local source in a river basin using a Neuro-fuzzy modeling tool. Journal of Environmental Management, 85(1), 215–223.Google Scholar
  4. Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. Operations Research Quarterly, 20, 451–468.Google Scholar
  5. Behrang, M. A., Assareh, E., Ghanbarzadeh, A., & Noghrehabadi, A. R. (2010). The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Solar Energy, 84(8), 1468–1480.Google Scholar
  6. Benediktsson, J. A., Sveinsson, J. R., Ersoy, O. K., & Swain, P. H. (1997). Parallel consensual neural networks. IEEE Transactions on Neural Networks, 8(1), 54–64.Google Scholar
  7. Benghanem, M., Mellit, A., & Alamri, S. N. (2009). ANN-based modelling & estimation of daily global solar radiation data: A case study. Energy Conversion and Management, 50(7), 1644–1655.Google Scholar
  8. Besharat, F., Dehghan, A. A., & Faghih, A. R. (2013). Empirical models for estimating global solar radiation: A review and case study. Renewable and Sustainable Energy Reviews, 21, 798–821.Google Scholar
  9. Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.Google Scholar
  10. Chen, R., Ersi, K., Yang, J., Lu, S., & Zhao, W. (2004). Validation of five global radiation models with measured daily data in China. Energy Conversion and Management, 45(11–12), 1759–1769.Google Scholar
  11. Elagib, N. A., & Mansell, M. G. (2000). New approaches for estimating global solar radiation across Sudan. Energy Conversion and Management, 41(5), 419–434.Google Scholar
  12. Hargreaves, G. H., & Samani, Z. A. (1982). Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, 108(3), 225–230.Google Scholar
  13. Hassan, G. E., Youssef, M. E., Mohamed, Z. E., Ali, M. A., & Hanafy, A. A. (2016). New temperature-based models for predicting global solar radiation. Applied Energy, 179, 437–450.Google Scholar
  14. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359–366.Google Scholar
  15. Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.Google Scholar
  16. Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing—A computational approach to learning and machine intelligence. Upper Saddle River, NJ: Prentice Hall.Google Scholar
  17. Kalogirou, S. A. (2001). Artificial neural networks in renewable energy systems applications: A review. Renewable and Sustainable Energy Review, 5(4), 373–401.Google Scholar
  18. Kiran, N. R., & Ravi, V. (2008). Software reliability prediction by soft computing techniques. Journal of Systems and Software, 81(4), 576–583.Google Scholar
  19. Koca, A., Oztop, H. F., Varol, Y., & Koca, G. O. (2011). Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey. Expert Systems with Applications, 38(7), 8756–8762.Google Scholar
  20. Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241.Google Scholar
  21. Li, M. F., Fan, L., Liu, H. B., Guo, P. T., & Wu, W. (2013a). A general model for estimation of daily global solar radiation using air temperatures and site geographic parameters in Southwest China. Journal of Atmospheric and Solar-Terrestrial Physics, 92, 145–150.Google Scholar
  22. Li, M. F., Tang, X. P., Wu, W., & Liu, H. B. (2013b). General models for estimating daily global solar radiation for different solar radiation zones in mainland China. Energy Conversion and Management, 70, 139–148.Google Scholar
  23. Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., et al. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111–153.Google Scholar
  24. Meenal, R., & Selvakumar, A. I. (2018). Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renewable Energy, 121, 324–343.Google Scholar
  25. Mellit, A. (2008). Artificial Intelligence technique for modelling and forecasting of solar radiation data: A review. International Journal of Artificial intelligence and Soft Computing, 1(1), 52–76.Google Scholar
  26. Mellit, A., Arab, A. H., Khorissi, N., & Salhi, H. (2007). An ANFIS-based forecasting for solar radiation data from sunshine duration and ambient temperature. In Power engineering society general meeting (pp. 1–6). IEEE.Google Scholar
  27. Meza, F., & Varas, E. (2000). Estimation of mean monthly solar global radiation as a function of temperature. Agricultural and Forest Meteorology, 100(2–3), 231–241.Google Scholar
  28. Mohammadi, K., Shamshirband, S., Tong, C. W., Arif, M., Petković, D., & Ch, S. (2015). A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion and Management, 92, 162–171.Google Scholar
  29. Mohanty, S. (2014). ANFIS based prediction of monthly average global solar radiation over Bhubaneswar (State of Odisha). International Journal Ethics in Engineering and Management Education, 1(5), 2348–4748.Google Scholar
  30. Mohanty, S., Patra, P. K., & Sahoo, S. S. (2016). Prediction and application of solar radiation with soft computing over traditional and conventional approach—A comprehensive review. Renewable and Sustainable Energy Reviews, 56, 778–796.Google Scholar
  31. Mokhtari, M., & Behnia, M. (2019). Comparison of LLNF, ANN, and COA-ANN techniques in modeling the uniaxial compressive strength and static Young’s Modulus of limestone of the Dalan formation. Natural Resources Research, 28(1), 223–239.Google Scholar
  32. Moreno, A., Gilabert, M. A., & Martínez, B. (2011). Mapping daily global solar irradiation over Spain: A comparative study of selected approaches. Solar Energy, 85(9), 2072–2084.Google Scholar
  33. Nourani, V., & Fard, M. S. (2012). Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advances in Engineering Software, 47(1), 127–146.Google Scholar
  34. Nourani, V., Khanghah, T. R., & Baghanam, A. H. (2015). Application of entropy concept for input selection of wavelet-ANN based rainfall-runoff modeling. Journal of Environmental Informatics, 26(1), 52–70.Google Scholar
  35. Nourani, V., & Komasi, M. (2013). A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. Journal of Hydrology, 490, 41–55.Google Scholar
  36. Nourani, V., Mousavi, S., Dabrowska, D., & Sadikoglu, F. (2017a). Conjunction of radial basis function interpolator and artificial intelligence models for time-space modeling of contaminant transport in porous media. Journal of Hydrology, 548, 569–587.Google Scholar
  37. Nourani, V., Mousavi, S., Sadikoglu, F., & Singh, V. P. (2017b). Experimental and AI-based numerical modeling of contaminant transport in porous media. Journal of Contaminant Hydrology, 205, 78–95.Google Scholar
  38. Perrone, M. P., & Cooper, L. N. (1993). Learning from what’s been learned: Supervised learning in multi-neural network systems. In Proceedings of the World Congress on Neural Networks (pp. 354–357). Portland, OR.Google Scholar
  39. Piri, J., & Kisi, O. (2015). 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, 39–47.Google Scholar
  40. Rahimikhoob, A. (2010). Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renewable Energy, 35(9), 2131–2135.Google Scholar
  41. Rehman, S., & Mohandes, M. (2008). Artificial neural network estimation of global solar radiation using air temperature and relative humidity. Energy Policy, 36(2), 571–576.Google Scholar
  42. Rohani, A., Taki, M., & Abdollahpour, M. (2018). A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renewable Energy, 115, 411–422.Google Scholar
  43. Roshanravan, B., Aghajani, H., Yousefi, M., & Kreuzer, O. (2018). Particle swarm optimization algorithm for neuro-fuzzy prospectivity analysis using continuously weighted spatial exploration data. Natural Resources Research.  https://doi.org/10.1007/s11053-018-9385-4.Google Scholar
  44. Salcedo-Sanz, S., Deo, R. C., Cornejo-Bueno, L., Camacho-Gómez, C., & Ghimire, S. (2018). An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia. Applied Energy, 209, 79–94.Google Scholar
  45. Sarlak, N., & Agha, O. M. M. (2018). Spatial and temporal variations of aridity indices in Iraq. Theoretical and Applied Climatology, 133(1–2), 89–99.Google Scholar
  46. Sharghi, E., Nourani, V., & Behfar, N. (2018). Earthfill dam seepage analysis using ensemble artificial intelligence based modeling. Journal of Hydroinformatics, 20(5), 1071–1084.Google Scholar
  47. Sharifi, S. S., Rezaverdinejad, V., & Nourani, V. (2016). Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches. Journal of Atmospheric and Solar-Terrestrial Physics, 149, 131–145.Google Scholar
  48. Tymvios, F. S., Jacovides, C. P., Michaelides, S. C., & Scouteli, C. (2005). Comparative study of Ångström’s and artificial neural networks’ methodologies in estimating global solar radiation. Solar Energy, 78(6), 752–762.Google Scholar
  49. Yacef, R., Benghanem, M., & Mellit, A. (2012). Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study. Renewable Energy, 48, 146–154.Google Scholar
  50. Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using artificial neural network techniques: A review. Renewable and Sustainable Energy Reviews, 33, 772–781.Google Scholar
  51. Yamashkin, S., Radovanovic, M., Yamashkin, A., & Vukovic, D. (2018). Using ensemble systems to study natural processes. Journal of Hydroinformatics, 20(4), 753–765.Google Scholar
  52. Yu, L., Wang, S., & Lai, K. K. (2005). A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates. Computers & Operations Research, 32(10), 2523–2541.Google Scholar
  53. Zhao, N., Zeng, X., & Han, S. (2013). Solar radiation estimation using sunshine hour and air pollution index in China. Energy Conversion and Management, 76, 846–851.Google Scholar

Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.Department of Water Resources Engineering, Faculty of Civil EngineeringUniversity of TabrizTabrizIran
  2. 2.Faculty of Civil and Environmental EngineeringNear East UniversityNicosiaTurkey
  3. 3.Department of Civil Engineering, Faculty of EngineeringIshik UniversityErbilIraq

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