A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)

The main objective of this paper is to present Gaussian Process Regression (GPR) as a new accurate soft computing model to predict daily and monthly solar radiation at Mashhad city, Iran. For this purpose, metrological data was collected from Iranian Meteorological Organization for Mashhad city located at the North-East for the period of 2009–2014. All the collected data include of maximum, minimum and average daily outdoor temperature (Tmax, Tmin and Tave), daily relative outdoor humidity (Rh), daily sea level pressure (p), day of a year (N), sunshine hours (Ns), daily extraterrestrial radiation on horizontal surface (H0) and daily global solar radiation on horizontal surface (H). Results of sensitivity analysis showed that (N/Ns, Tave, Rh, H0) is the best data set group for evaluation of daily global solar radiation at this region. For the GPR model, MAPE, RMSE and EF were 1.97%, 0.16 and 0.99, respectively. Monthly evaluation showed that the main model is not suitable for every month, so for every month, perfect model was trained and tested. Generalizability and stability of the GPR model was evaluated by different sizes of training data with 5-fold analysis. The results showed that GPR model can use with small size of data groups.

[1]  Sthitapragyan Mohanty,et al.  Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review , 2016 .

[2]  K.G.T. Hollands,et al.  Relationship between sunshine duration and solar radiation , 2013 .

[3]  Saeid Mehdizadeh,et al.  Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation , 2016 .

[4]  Neelamegam Premalatha,et al.  Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms , 2016 .

[5]  Tae-Hyung Kim,et al.  Feature selection for manufacturing process monitoring using cross-validation , 2013 .

[6]  Nilesh Kumar,et al.  Prediction of Solar Energy Based on Intelligent ANN Modeling , 2016, International Journal of Renewable Energy Research.

[7]  T. Meretoja,et al.  Cross-validation of three predictive tools for non-sentinel node metastases in breast cancer patients with micrometastases or isolated tumor cells in the sentinel node. , 2014, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[8]  Yingni Jiang,et al.  Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models , 2009 .

[9]  Javier Almorox,et al.  Global solar radiation estimation using sunshine duration in Spain , 2004 .

[10]  أحمد عبد العظيم السباعي,et al.  Global, direct and diffuse solar radiation on horizontal and tilted surfaces in Jeddah, Saudi Arabia , 2010 .

[11]  Carlo Renno,et al.  Artificial neural network models for predicting the solar radiation as input of a concentrating photovoltaic system , 2015 .

[12]  A. A. El-Sebaii,et al.  Estimation of horizontal diffuse solar radiation in Egypt , 2003 .

[13]  T. Cullen,et al.  Global existence of solutions for the relativistic Boltzmann equation on the flat Robertson-Walker space-time for arbitrarily large intial data , 2005, gr-qc/0507035.

[14]  Saeed-Reza Sabbagh-Yazdi,et al.  Evaluating the effect of particulate matter pollution on estimation of daily global solar radiation using artificial neural network modeling based on meteorological data , 2017 .

[15]  Srđan Jović,et al.  Solar radiation analyzing by neuro-fuzzy approach , 2016 .

[16]  Badia Amrouche,et al.  Artificial neural network based daily local forecasting for global solar radiation , 2014 .

[17]  Khubaib Amjad Alam,et al.  Support vector regression based prediction of global solar radiation on a horizontal surface , 2015 .

[18]  Gasser E. Hassan,et al.  New Temperature-based Models for Predicting Global Solar Radiation , 2016 .

[19]  Zhenzhen Zhao,et al.  Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China , 2016 .

[20]  J. Bakhashwain,et al.  Prediction of global solar radiation using support vector machines , 2016 .

[21]  E. A. Ogujor,et al.  Evaluation of various global solar radiation models for Nigeria , 2016 .

[22]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[23]  A. Kamsin,et al.  Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure , 2016 .

[24]  Huashan Li,et al.  Estimating daily global solar radiation by day of year in China , 2010 .

[25]  Weiliang Fan,et al.  A method for daily global solar radiation estimation from two instantaneous values using MODIS atmospheric products , 2016 .

[26]  Gasser E. Hassan,et al.  Performance assessment of different day-of-the-year-based models for estimating global solar radiation - Case study: Egypt , 2016 .

[27]  A. Regattieri,et al.  Artificial neural network optimisation for monthly average daily global solar radiation prediction , 2016 .

[28]  F. Besharat,et al.  Empirical models for estimating global solar radiation: A review and case study , 2013 .

[29]  O. Kisi,et al.  Solar radiation prediction using different techniques: model evaluation and comparison , 2016 .

[30]  Yılmaz Kaya,et al.  Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data , 2013 .

[31]  Dmitri Kavetski,et al.  A new stochastic model for simulating daily solar radiation from sunshine hours , 2015 .

[32]  Majid Jamil,et al.  Empirical correlation of estimating global solar radiation using meteorological parameters , 2015 .

[33]  C. W. Tong,et al.  A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation , 2015 .

[34]  Saad Mekhilef,et al.  Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria , 2015 .

[35]  Ayad Almryad,et al.  Modeling of solar energy potential in Libya using an artificial neural network model , 2016, 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP).

[36]  D. Fadare.  Modelling of solar energy potential in Nigeria using an artificial neural network model , 2009 .

[37]  Por Lip Yee,et al.  Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model , 2016 .

[38]  Joseph A. Jervase,et al.  Solar radiation estimation using artificial neural networks , 2002 .

[39]  Cyril Voyant,et al.  Bayesian rules and stochastic models for high accuracy prediction of solar radiation , 2013, ArXiv.

[40]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[41]  Kasra Mohammadi,et al.  A support vector machine–firefly algorithm-based model for global solar radiation prediction , 2015 .

[42]  Ahmet Teke,et al.  Evaluation and performance comparison of different models for the estimation of solar radiation , 2015 .

[43]  Laurel Saito,et al.  ANFIS, SVM and ANN soft-computing techniques to estimate daily global solar radiation in a warm sub-humid environment , 2017 .

[44]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[45]  Ping Jiang,et al.  Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation , 2016, Neurocomputing.

[46]  Dalibor Petković,et al.  Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year , 2015 .

[47]  Annette Hammer,et al.  Estimation of daily global solar irradiation by coupling ground measurements of bright sunshine hours to satellite imagery , 2013 .

[48]  Sara Saeidi Ramiyani,et al.  A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran , 2013 .

[49]  Xulin Guo,et al.  Predicting daily photosynthetically active radiation from global solar radiation in the Contiguous United States , 2015 .

[50]  Adnan Sözen,et al.  Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data , 2004 .

[51]  Ali Rahimikhoob,et al.  Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment , 2010 .

[52]  Hasmat Malik,et al.  Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India , 2015 .

[53]  Ozgur Kisi,et al.  Modelling solar radiation reached to the Earth using ANFIS, NN-ARX, and empirical models (Case studies: Zahedan and Bojnurd stations) , 2015 .

[54]  Rajesh Kumar,et al.  Comparison of regression and artificial neural network models for estimation of global solar radiations , 2015 .

[55]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[56]  Laurel Saito,et al.  Empirical models for estimating daily global solar radiation in Yucatán Peninsula, Mexico , 2016 .

[57]  Shahaboddin Shamshirband,et al.  Daily global solar radiation prediction from air temperatures using kernel extreme learning machine: A case study for Iran , 2015 .

[58]  Adel Mellit,et al.  Prediction of daily global solar irradiation data using Bayesian neural network: A comparative study , 2012 .

[59]  Zhong Yi Wan,et al.  Reduced-space Gaussian Process Regression for data-driven probabilistic forecast of chaotic dynamical systems , 2016, 1611.01583.

[60]  Guangjian Yan,et al.  Estimation of surface shortwave radiation components under all sky conditions: Modeling and sensitivity analysis , 2012 .

[61]  Paulo Salgado,et al.  Prediction of Solar Radiation Using Artificial Neural Networks , 2015 .

[62]  Vahid Nourani,et al.  Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches , 2016 .

[63]  F. Hocaoglu,et al.  A novel adaptive approach for hourly solar radiation forecasting , 2016 .

[64]  Detlev Heinemann,et al.  Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks , 2016 .

[65]  Ahmet Teke,et al.  The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in Eastern Mediterranean Region of Turkey , 2016 .

[66]  Cyril Voyant,et al.  Hybrid methodology for hourly global radiation forecasting in Mediterranean area , 2012, ArXiv.

[67]  Shahaboddin Shamshirband,et al.  Prediction of the solar radiation on the Earth using support vector regression technique , 2015 .

[68]  Abbas Rohani,et al.  Heat transfer and MLP neural network models to predict inside environment variables and energy lost in a semi-solar greenhouse , 2016 .

[69]  Amrita Das,et al.  Estimation of available global solar radiation using sunshine duration over South Korea , 2015 .

[70]  Gilles Notton,et al.  Neural network approach to estimate 10-min solar global irradiation values on tilted planes , 2013 .

[71]  Dawei Han,et al.  An improved technique for global solar radiation estimation using numerical weather prediction , 2015 .