Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting

Abstract Global radiation is not a regularly measured parameter in any weather station relative to other meteorological parameters due to measurement costs. This study has proposed hybrid artificial neural network models that predicted monthly radiation using typical weather and geographic data. Two datasets and six artificial neural network models were respectively built for indigenous and widespread regions around the world. The referred models co-optimized the artificial neural network properties and feature selection. For this purpose, an adaptive evolutionary algorithm improving prediction performance was developed to train the neural networks. This novel approach has yielded promising results compared to the developed deep learning models in this study. The results revealed that while the indigenous models had common features of longitude, sunshine durations, precipitation, and wind speed, the widespread models involved those of latitude, sunshine durations, and mean daily maximum air temperature. The proposed hybrid model had respectively the best mean absolute percentage errors of 2.45% and 9.93% for validation dataset and 3.75% and 11.03% for testing dataset of the indigenous and widespread regions, respectively. The present findings showed that the proposed hybrid model could be evaluated as a generic model and could improve the forecasting accuracy with the specified optimization parameters.

[1]  Saleh M. Al-Alawi,et al.  An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation , 1998 .

[2]  Mohamed Mohandes,et al.  USE OF RADIAL BASIS FUNCTIONS FOR ESTIMATING MONTHLY MEAN DAILY SOLAR RADIATION , 2000 .

[3]  Viorel Badescu,et al.  A current perspective on the accuracy of incoming solar energy forecasting , 2019, Progress in Energy and Combustion Science.

[4]  Yu Zhang,et al.  Prediction of solar radiation with genetic approach combing multi-model framework , 2014 .

[5]  Pengcheng Jiao,et al.  Next generation prediction model for daily solar radiation on horizontal surface using a hybrid neural network and simulated annealing method , 2017 .

[6]  M. Ranjan,et al.  Solar resource estimation using artificial neural networks and comparison with other correlation models , 2003 .

[7]  P. A. Rocha,et al.  Estimation of daily, weekly and monthly global solar radiation using ANNs and a long data set: a case study of Fortaleza, in Brazilian Northeast region , 2019, International Journal of Energy and Environmental Engineering.

[8]  Shahaboddin Shamshirband,et al.  Hybrid auto-regressive neural network model for estimating global solar radiation in Bandar Abbas, Iran , 2016, Environmental Earth Sciences.

[9]  İ. Yılmaz,et al.  An innovative dynamic model for an integrated solar combined cycle power plant under off-design conditions , 2020 .

[10]  İ. Yılmaz,et al.  Enhancing the overall thermal performance of a large aperture parabolic trough solar collector using wire coil inserts , 2020 .

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

[12]  Jorge Aguilera,et al.  An application of the multilayer perceptron: Solar radiation maps in Spain , 2005 .

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

[14]  M. Söylemez,et al.  Performance Testing of A Parabolic Trough Collector Array , 2015 .

[15]  J. Mubiru,et al.  Estimation of monthly average daily global solar irradiation using artificial neural networks , 2008 .

[16]  İbrahim Halil Yılmaz,et al.  Modeling, simulation and performance analysis of parabolic trough solar collectors: A comprehensive review , 2018, Applied Energy.

[17]  E. Arcaklioğlu,et al.  Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .

[18]  F. S. Tymvios,et al.  Comparative study of Ångström's and artificial neural networks' methodologies in estimating global solar radiation , 2005 .

[19]  Cyril Voyant,et al.  Optimization of an artificial neural network dedicated to the multivariate forecasting of daily glob , 2011 .

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

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

[22]  Taghi M. Khoshgoftaar,et al.  Deep learning applications and challenges in big data analytics , 2015, Journal of Big Data.

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

[24]  Gianpaolo Vitale,et al.  Solar and wind forecasting by NARX neural networks , 2016 .

[25]  Amit Kumar Yadav,et al.  Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model , 2015 .

[26]  Muammer Ozgoren,et al.  Estimation of global solar radiation using ANN over Turkey , 2012, Expert Syst. Appl..

[27]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[28]  O. Şenkal.  Modeling of solar radiation using remote sensing and artificial neural network in Turkey , 2010 .

[29]  O. Şenkal,et al.  Estimation of solar radiation over Turkey using artificial neural network and satellite data , 2009 .

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

[31]  Cristina Cornaro,et al.  Master optimization process based on neural networks ensemble for 24-h solar irradiance forecast , 2015 .

[32]  S Kumar,et al.  Efficient solar radiation estimation using cohesive artificial neural network technique with optimal synaptic weights , 2020, Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy.

[33]  Karoro Angela,et al.  Predicting global solar radiation using an artificial neural network single-parameter model , 2011 .

[34]  K. Kaba,et al.  Estimation of daily global solar radiation using deep learning model , 2018, Energy.

[35]  Mohamed Mohandes,et al.  Estimation of global solar radiation using artificial neural networks , 1998 .

[36]  M. Mohandes.  Modeling global solar radiation using Particle Swarm Optimization (PSO) , 2012 .

[37]  Adnan Sözen,et al.  Forecasting based on neural network approach of solar potential in Turkey , 2005 .

[38]  İ. Yılmaz.  Residential use of solar water heating in Turkey: A novel thermo-economic optimization for energy savings, cost benefit and ecology , 2018, Journal of Cleaner Production.

[39]  Yves Gagnon,et al.  Estimation of Monthly Mean Daily Global Solar Radiation over Bangkok, Thailand Using Artificial Neural Networks , 2014 .

[40]  İ. Yılmaz,et al.  Thermal and thermodynamic benchmarking of liquid heat transfer fluids in a high concentration ratio parabolic trough solar collector system , 2020 .

[41]  İ. Yılmaz,et al.  Numerical analysis of the thermal and thermodynamic performance of a parabolic trough solar collector using SWCNTs-Therminol®VP-1 nanofluid , 2018 .

[42]  Ali Azadeh,et al.  An integrated artificial neural networks approach for predicting global radiation , 2009 .

[43]  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 .

[44]  S. Chandel,et al.  Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models , 2014 .

[45]  Sancho Salcedo-Sanz,et al.  Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach , 2014 .

[46]  Soteris A. Kalogirou,et al.  Artificial intelligence techniques for photovoltaic applications: A review , 2008 .

[47]  Jaekyung Yang,et al.  Optimization-based feature selection with adaptive instance sampling , 2006, Comput. Oper. Res..

[48]  ADNAN SOZEN,et al.  A Study for Estimating Solar Resources in Turkey Using Artificial Neural Networks , 2004 .

[49]  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 .

[50]  A. Assi,et al.  ARTIFICIAL NEURAL NETWORKS FOR PREDICTING GLOBAL SOLAR RADIATION IN AL AIN CITY - UAE , 2013 .

[51]  Bin Zhou,et al.  Solar irradiance forecasting based on direct explainable neural network , 2020 .

[52]  Sajid Hussain,et al.  A pruning approach to optimize synaptic connections and select relevant input parameters for neural network modelling of solar radiation , 2017, Appl. Soft Comput..

[53]  I. Teke,et al.  Global solar radiation estimation from measurements of visibility and air temperature extremes , 2018, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects.

[54]  V SIVAMADHAVI,et al.  Prediction of monthly mean daily global solar radiation using Artificial Neural Network , 2012, Journal of Earth System Science.

[55]  Pedro Antonio Gutiérrez,et al.  A novel approach for global solar irradiation forecasting on tilted plane using Hybrid Evolutionary Neural Networks , 2021 .

[56]  J. Sanz-Justo,et al.  A CRO-species optimization scheme for robust global solar radiation statistical downscaling , 2017 .

[57]  Yuansheng Yang,et al.  A Wrapper Feature Subset Selection Method Based on Randomized Search and Multilayer Structure , 2019, BioMed research international.

[58]  Ahmet Koca,et al.  Estimation of solar radiation using artificial neural networks with different input parameters for Mediterranean region of Anatolia in Turkey , 2011, Expert Syst. Appl..

[59]  Khalid Zenkouar,et al.  Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data , 2020 .

[60]  Mehmet Sait Söylemez,et al.  Design and computer simulation on multi-effect evaporation seawater desalination system using hybrid renewable energy sources in Turkey , 2012 .

[61]  Mehmet Sait Söylemez,et al.  Thermo-mathematical modeling of parabolic trough collector , 2014 .

[62]  Mamdouh El Haj Assad,et al.  Comparison of artificial intelligence methods in estimation of daily global solar radiation , 2018, Journal of Cleaner Production.

[63]  J. Sanz-Justo,et al.  A novel Grouping Genetic Algorithm–Extreme Learning Machine approach for global solar radiation prediction from numerical weather models inputs , 2016 .

[64]  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 .

[65]  Farid Melgani,et al.  A comprehensive review of hybrid models for solar radiation forecasting , 2020 .

[66]  Adel Mellit,et al.  A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach , 2005 .