A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study

Highly accurate estimating of daily solar radiation by developing an intelligent and robust model has been a subject of prominent concern for many researchers in the past few years. The precise prediction of solar radiation is of great interest and importance to improve the incorporation of solar power plants. In this study, a novel multilayer framework for a particular combination of the bat algorithm (BA) and neural networks (NN) is proposed, which is called bat neural network (BNN), aimed at predicting daily solar radiation over Iran. For appraising the performance of the proposed BNN, daily solar radiation data from four cities of Iran including Jask, Kermanshah, Ramsar, and Tehran are analyzed. The results indicate that among the tested models, BNN gains the best performance in the prediction of daily solar radiation. Among various soft computing approaches, the BA, which is inspired by the nature of microbats’ behaviour, has a significant impact on the optimization of this study.

[1]  Ali Mostafaeipour,et al.  Economic evaluation for cooling and ventilation of medicine storage warehouses utilizing wind catchers , 2014 .

[2]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

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

[4]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[5]  Abdul Razak Hamdan,et al.  Multi-population cooperative bat algorithm-based optimization of artificial neural network model , 2015, Inf. Sci..

[6]  Ali Mostafaeipour,et al.  Evaluation of installing photovoltaic plants using a hybrid approach for Khuzestan province, Iran , 2016 .

[7]  Kasra Mohammadi,et al.  Evaluating the wind energy potential for hydrogen production: A case study , 2016 .

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

[9]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[10]  Ryozo Ooka,et al.  Metaheuristic optimization methods for a comprehensive operating schedule of battery, thermal energy storage, and heat source in a building energy system , 2015 .

[11]  Filippo Menczer,et al.  Feature selection in data mining , 2003 .

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

[13]  R. Hafezi,et al.  Sustainability in development: rethinking about old paradigms , 2017 .

[14]  Chandrasekhar Yammani,et al.  Optimal placement and sizing of distributed generations using shuffled bat algorithm with future load enhancement , 2016 .

[15]  Abdul Razak Hamdan,et al.  Optimization of neural network model using modified bat-inspired algorithm , 2015, Appl. Soft Comput..

[16]  Yan Su,et al.  Analysis of daily solar power prediction with data-driven approaches , 2014 .

[17]  R. Suganya,et al.  Data Mining Concepts and Techniques , 2010 .

[18]  Zhigang Zeng,et al.  A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm , 2017, Neurocomputing.

[19]  Esmaeil Hadavandi,et al.  A hybrid intelligent approach for modeling brand choice and constructing a market response simulator , 2013, Knowl. Based Syst..

[20]  Luca Delle Monache,et al.  Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting , 2016 .

[21]  Xin-She Yang,et al.  An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems , 2016, Eng. Appl. Artif. Intell..

[22]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[23]  H. Lian.  On feature selection with principal component analysis for one-class SVM , 2012, Pattern Recognit. Lett..

[24]  Leslie S. Smith,et al.  Feature subset selection in large dimensionality domains , 2010, Pattern Recognit..

[25]  A. Marzo,et al.  Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation , 2017 .

[26]  Gm. Shafiullah,et al.  Hybrid renewable energy integration (HREI) system for subtropical climate in Central Queensland, Australia , 2016 .

[27]  Jun S. Liu,et al.  Bayesian Clustering with Variable and Transformation Selections , 2003 .

[28]  W. Massy.  Principal Components Regression in Exploratory Statistical Research , 1965 .

[29]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[30]  Xin-She Yang,et al.  A New Metaheuristic Bat-Inspired Algorithm , 2010, NICSO.

[31]  David A. Wood,et al.  Hybrid bat flight optimization algorithm applied to complex wellbore trajectories highlights the relative contributions of metaheuristic components , 2016 .

[32]  Esmaeil Hadavandi,et al.  Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction , 2012, Knowl. Based Syst..

[33]  Shahaboddin Shamshirband,et al.  A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation , 2015 .

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

[35]  Reza Hafezi,et al.  A new hybrid decision framework for prioritizing funding allocation to Iran's energy sector , 2017 .

[36]  Shahaboddin Shamshirband,et al.  A novel Boosted-neural network ensemble for modeling multi-target regression problems , 2015, Eng. Appl. Artif. Intell..

[37]  Daniel O’Leary,et al.  Feature Selection and ANN Solar Power Prediction , 2017 .

[38]  Alistair A. Young,et al.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2017, MICCAI 2017.

[39]  E. Zavadskas,et al.  Forecasting gold price changes by using adaptive network fuzzy inference system , 2012 .

[40]  Mohammad Hassan Khooban,et al.  The online parameter identification of chaotic behaviour in permanent magnet synchronous motor by Self-Adaptive Learning Bat-inspired algorithm , 2016 .

[41]  Shahaboddin Shamshirband,et al.  Potential of radial basis function based support vector regression for global solar radiation prediction , 2014 .

[42]  George S. Atsalakis,et al.  Elliott Wave Theory and neuro-fuzzy systems, in stock market prediction: The WASP system , 2011, Expert Syst. Appl..

[43]  M. Kowsalya,et al.  Optimal allocation of solar based distributed generators in distribution system using Bat algorithm , 2016 .

[44]  Chiranjeevi Karri,et al.  Fast vector quantization using a Bat algorithm for image compression , 2016 .

[45]  Yoichi Hayashi,et al.  SPMoE: a novel subspace-projected mixture of experts model for multi-target regression problems , 2015, Soft Computing.

[46]  Harun Uguz,et al.  A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm , 2011, Knowl. Based Syst..

[47]  Subhas Ganguly,et al.  New training strategies for neural networks with application to quaternary Al-Mg-Sc-Cr alloy design problems , 2016, Appl. Soft Comput..

[48]  Wei Liu,et al.  A novel visual tracking method using bat algorithm , 2016, Neurocomputing.

[49]  Reza Hafezi,et al.  Forecasting Gold Price Changes: Application of an Equipped Artificial Neural Network , 2018 .

[50]  Junheung Park,et al.  Meta-modeling using generalized regression neural network and particle swarm optimization , 2017, Appl. Soft Comput..

[51]  Esmaeil Hadavandi,et al.  A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price , 2015, Appl. Soft Comput..

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

[53]  Kimon P. Valavanis,et al.  Surveying stock market forecasting techniques - Part II: Soft computing methods , 2009, Expert Syst. Appl..

[54]  Oguz Altun,et al.  A novel meta-heuristic algorithm: Dynamic Virtual Bats Algorithm , 2016, Inf. Sci..

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