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A new approach for model validation in solar radiation using wavelet, phase and frequency coherence analysis

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  • Hussain, Sajid
  • Al-Alili, Ali

Abstract

The performance of solar radiation models is evaluated based on time domain statistical metrics. These metrics include root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). However, this study adopts a new approach to model validation in phase and frequency domains. The study proposes frequency coherence and phase synchronization methods quantified into frequency coherence index (FCI) and phase lock value (PLV), respectively. To get more in-depth insight of the model’s performance, important visual indicators based on wavelet cross-spectrum (WCS) and wavelet coherence analysis (WCA) are presented. Two different widely used solar radiation models based on artificial neural network (ANN) namely, the multi-layer perceptron (MLP) and the adaptive neuro-fuzzy inference system (ANFIS), are analysed. The proposed techniques are used to exploit weaknesses and strengths of the models. Anomalies in phase and frequency components are identified and the information is used to increase their performance. The MLP is modified into a non-linear autoregressive recurrent exogenous neural networks (NARX-NN) using recursive filtering. Results show that NARX-NN corrects the phase differences, filters out the faulty frequency components and increases the time domain validation metric, i.e. R2.

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  • 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.
    • Handle: RePEc:eee:appene:v:164:y:2016:i:c:p:639-649
    DOI: 10.1016/j.apenergy.2015.12.038
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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. 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.
    4. 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.
    5. Dorvlo, Atsu S. S. & Jervase, Joseph A. & Al-Lawati, Ali, 2002. "Solar radiation estimation using artificial neural networks," Applied Energy, Elsevier, vol. 71(4), pages 307-319, April.
    6. Jamal, Wasifa & Das, Saptarshi & Maharatna, Koushik & Pan, Indranil & Kuyucu, Doga, 2015. "Brain connectivity analysis from EEG signals using stable phase-synchronized states during face perception tasks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 434(C), pages 273-295.
    7. Hassan, Jamal, 2014. "ARIMA and regression models for prediction of daily and monthly clearness index," Renewable Energy, Elsevier, vol. 68(C), pages 421-427.
    8. 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.
    9. Mahmoud, Emad E., 2013. "Modified projective phase synchronization of chaotic complex nonlinear systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 89(C), pages 69-85.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
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    7. Hassan, Muhammed A. & Khalil, A. & Kaseb, S. & Kassem, M.A., 2017. "Exploring the potential of tree-based ensemble methods in solar radiation modeling," Applied Energy, Elsevier, vol. 203(C), pages 897-916.

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