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A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks

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  • Hussain, Sajid
  • AlAlili, Ali

Abstract

Assessment of solar potential over a location of interest is an important step towards the successful planning of renewable energy projects. However, solar data are not available for every point of interest due to the absence of meteorological stations and sophisticated solar sensors, so solar radiation has to be estimated using models. This paper presents a hybrid technique to improve the performance of a widely used modeling technique i.e. artificial neural network (ANN). Four different architectures of ANN, namely: multilayer perceptron (MLP), Adaptive neuro-fuzzy inference system (ANFIS), Nonlinear autoregressive recurrent exogenous neural network (NARX), and generalized regression neural networks (GRNN), are used in this study. A wavelet multiresolution analysis is applied to decompose the complex meteorological signals into relatively simple parts, wavelet sub-series, using discrete wavelet transformation (DWT) algorithm. The wavelet sub-series are modeled by the ANN models and reconstructed to estimate the original signal. Hence, enhancing the learning process of these models. Four meteorological parameters, namely: temperature (T), relative humidity (RH), wind speed (WS), and sunshine duration (SSD), are used to mode the global horizontal irradiation (GHI) over Abu Dhabi, the United Arab Emirates. The proposed approach is compared to standalone ANN models and validated using well-known statistical validation metrics including coefficient of determination (R2), root mean square error (RMSE), mean bias error (MBE), mean absolute percentage error (MAPE), and t-statistics. In addition, wavelet cross spectrum (WCS) is used as a visual indicator of the model performance in time, frequency, and phase domains. The results show that using the proposed strategy considerably improves the modeling performance of the ANN with a maximum improvement of 6.84% in R2 for MLP. In addition, minimum RMSE of 2.78% is observed for GRNN.

Suggested Citation

  • Hussain, Sajid & AlAlili, Ali, 2017. "A hybrid solar radiation modeling approach using wavelet multiresolution analysis and artificial neural networks," Applied Energy, Elsevier, vol. 208(C), pages 540-550.
    • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:540-550
    DOI: 10.1016/j.apenergy.2017.09.100
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    1. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    2. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    3. Hussain, Sajid & Al-Alili, Ali, 2016. "A new approach for model validation in solar radiation using wavelet, phase and frequency coherence analysis," Applied Energy, Elsevier, vol. 164(C), pages 639-649.
    4. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    5. Mostafavi, Elham Sadat & Ramiyani, Sara Saeidi & Sarvar, Rahim & Moud, Hashem Izadi & Mousavi, Seyyed Mohammad, 2013. "A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran," Energy, Elsevier, vol. 49(C), pages 204-210.
    6. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    7. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    8. Voyant, Cyril & Darras, Christophe & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure & Poggi, Philippe, 2014. "Bayesian rules and stochastic models for high accuracy prediction of solar radiation," Applied Energy, Elsevier, vol. 114(C), pages 218-226.
    9. Lu, Ning & Qin, Jun & Yang, Kun & Sun, Jiulin, 2011. "A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data," Energy, Elsevier, vol. 36(5), pages 3179-3188.
    10. Chellali, F. & Khellaf, A. & Belouchrani, A., 2010. "Wavelet spectral analysis of the temperature and wind speed data at Adrar, Algeria," Renewable Energy, Elsevier, vol. 35(6), pages 1214-1219.
    11. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    12. Chang, Tian-Pau & Liu, Feng-Jiao & Ko, Hong-Hsi & Huang, Ming-Chao, 2017. "Oscillation characteristic study of wind speed, global solar radiation and air temperature using wavelet analysis," Applied Energy, Elsevier, vol. 190(C), pages 650-657.
    13. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
    14. Aksakal, Ahmet & Rehman, Shafiqur, 1999. "Global solar radiation in Northeastern Saudi Arabia," Renewable Energy, Elsevier, vol. 17(4), pages 461-472.
    15. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    16. Yadav, Amit Kumar & Chandel, S.S., 2015. "Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model," Renewable Energy, Elsevier, vol. 75(C), pages 675-693.
    17. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2014. "Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 509-519.
    18. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    19. Jacovides, C. P. & Kontoyiannis, H., 1995. "Statistical procedures for the evaluation of evapotranspiration computing models," Agricultural Water Management, Elsevier, vol. 27(3-4), pages 365-371, July.
    20. Bosch, J.L. & López, G. & Batlles, F.J., 2008. "Daily solar irradiation estimation over a mountainous area using artificial neural networks," Renewable Energy, Elsevier, vol. 33(7), pages 1622-1628.
    21. Alam, Shah & Kaushik, S.C. & Garg, S.N., 2009. "Assessment of diffuse solar energy under general sky condition using artificial neural network," Applied Energy, Elsevier, vol. 86(4), pages 554-564, April.
    22. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
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