Assessing the proficiency of adaptive neuro-fuzzy system to estimate wind power density: Case study of Aligoodarz, Iran

Shamshirband, Shahaboddin,Keivani, Afram,Mohammadi, Kasra,Lee, Malrey,Hamid, Siti Hafizah Abd,Petkovic, Dalibor Elsevier 2016 RENEWABLE & SUSTAINABLE ENERGY REVIEWS Vol.59 No.-

<P><B>Abstract</B></P> <P>The prime aim of this study is appraising the suitability of adaptive neuro-fuzzy inference framework (ANFIS) to compute the monthly wind power density. On this account, the extracted wind power from Weibull functions are utilized for training and testing the developed ANFIS model. The proficiency of the ANFIS model is certified by providing thorough statistical comparisons with artificial neural network (ANN) and genetic programming (GP) techniques. The computed wind power by all models are compared with those obtained using measured data. The study results clearly indicate that the proposed ANFIS model enjoys high capability and reliability to estimate wind power density so that it presents high superiority over the developed ANN and GP models. Based upon relative percentage error (RPE) values, all estimated wind power values via ANFIS model are within the acceptable range of −10% to 10%. Additionally, relative root mean square error (RRMSE) analysis shows that ANFIS model has an excellent performance for estimation of wind power density.</P>

Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model

Shamshirband, Shahaboddin,Mohammadi, Kasra,Khorasanizadeh, Hossein,Yee, Por Lip,Lee, Malrey,Petković,, Dalibor,Zalnezhad, Erfan Elsevier 2016 RENEWABLE & SUSTAINABLE ENERGY REVIEWS Vol.56 No.-

<P><B>Abstract</B></P> <P>Diffuse solar radiation is a fundamental parameter highly required in several solar energy applications. Despite its significance, diffuse solar radiation is not measured in many locations around the world due to technical and fiscal limitations. On this account, determining the amount of diffuse radiation alternatively based upon precise and reliable estimating methods is indeed essential. In this paper, a coupled model is developed for estimating daily horizontal diffuse solar radiation by integrating the support vector machine (SVM) with wavelet transform (WT) algorithm. To test the validity of the coupled SVM–WT method, daily measured global and diffuse solar radiation data sets for city of Kerman situated in a sunny part of Iran are utilized. For the developed SVM–WT model, diffuse fraction (cloudiness index) is correlated with clearness index as the only input parameter. The suitability of SVM–WT is evaluated against radial basis function SVM (SVM–RBF), artificial neural network (ANN) and a 3rd degree empirical model established for this study. It is found that the estimated diffuse solar radiation values by the SVM–WT model are in favourable agreements with measured data. According to the conducted statistical analysis, the obtained mean absolute bias error, root mean square error and correlation coefficient are 0.5757MJ/m<SUP>2</SUP>, 0.6940MJ/m<SUP>2</SUP> and 0.9631, respectively. While for the SVM–RBF ranked next the attained values are 1.0877MJ/m<SUP>2</SUP>, 1.2583MJ/m<SUP>2</SUP> and 0.8599, respectively. In fact, the study results indicate that SVM–WT is an efficient method which enjoys much higher precision than other models, especially the 3rd degree empirical model.</P>

Improved Side Weir Discharge Coefficient Modeling by Adaptive Neuro-fuzzy Methodology

Shahaboddin Shamshirband,Hossein Bonakdari,Amir Hossein Zaji,Dalibor Petkovic,Shervin Motamedi 대한토목학회 2016 KSCE JOURNAL OF CIVIL ENGINEERING Vol.20 No.7

In this article, the accuracy of a soft computing technique is evaluated in terms of discharge coefficient prediction of an improved triangular side weir. The process includes simulating the discharge coefficient with the Adaptive Neuro-Fuzzy Inference System (ANFIS). Matlab software is used for ANFIS modeling. To identify the most appropriate input variables, eight different input combinations with various numbers of inputs are examined. The performance of the proposed system is confirmed by comparing the ANFIS and experimental results for the testing dataset. The performance evaluation demonstrates that the ANFIS model with five inputs (Root Mean Square Error (RMSE) of 0.014) is more accurate than the ANFIS model with one input (RMSE = 0.088). The ANFIS model results are also compared with the results obtained from previous regression and soft computing studies.

RETRACTED: Survey of the most influential parameters on the wind farm net present value (NPV) by adaptive neuro-fuzzy approach

Petković,, Dalibor,Shamshirband, Shahaboddin,Kamsin, Amirrudin,Lee, Malrey,Anicic, Obrad,Nikolić,, Vlastimir Elsevier 2016 RENEWABLE & SUSTAINABLE ENERGY REVIEWS Vol.57 No.-

<P>This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal).</P> <P>This article has been retracted at the request of Editor-in-Chief.</P> <P>This article bears substantial similarity to previously published works, including</P> <P>1. 'Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system'; Dalibor Petković, Žarko Ćojbašić, Stevo Lukić; Expert Systems with Applications, Volume 40, Issue 11, 1 September 2013, Pages 4490-4495, 10.1016/j.eswa.2013.01.055 </P> <P>2. 'Optimization of wind turbine micrositing: A comparative study'; Samina Rajper, Imran J. Amin; Renewable and Sustainable Energy Reviews, Volume 16, Issue 8, October 2012, Pages 5485–5492, 10.1016/j.rser.2012.06.014.</P> <P>One of the conditions of submission of a paper for publication is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. As such this article represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of the journal that this was not detected during the submission or review process..</P> <P>Renewable and Sustainable Energy Reviews (2016) Page range from 1270 - 1278, 10.1016/j.rser.2015.12.175 </P>

Optimal location planning to install wind turbines for hydrogen production: A case study

Mostafaeipour, Ali,Arabi, Fateme,Qolipour, Mojtaba,Shamshirband, Shahaboldin,Alavi, Omid Techno-Press 2017 Advances in energy research Vol.5 No.2

This study aims to evaluate and prioritize ten different sites in Iran's Khorasan provinces for the construction of wind farm. After studying the geography of the sites, nine criteria; including wind power, topography, wind direction, population, distance from power grid, level of air pollution, land cost per square meter, rate of natural disasters, and distance from road network-are selected for the analysis. Prioritization is performed using data envelopment analysis (DEA). The developed DEA model is validated through value engineering based on the results of brainstorming sessions. The results show that the order of priority of ten assessed candidate sites for installing wind turbines is Khaf, Afriz, Ghadamgah, Fadashk, Sarakhs, Bojnoord, Nehbandan, Esfarayen, Davarzan, and Roudab. Additionally, the outcomes extracted from the value engineering method identify the city of Khaf as the best candidate site. Six different wind turbines (7.5 to 5,000 kW) are considered in this location to generate electricity. Regarding an approach to produce and store hydrogen from wind farm installed in the location, the AREVA M5000 wind turbine can produce approximately $337ton-H_2$ over a year. It is an enormous amount that can be used in transportation and other industries.

Localization of solar-hydrogen power plants in the province of Kerman, Iran

Mostafaeipour, Ali,Sedaghat, Ahmad,Qolipour, Mojtaba,Rezaei, Mostafa,Arabnia, Hamid R.,Saidi-Mehrabad, Mohammad,Shamshirband, Shahaboddin,Alavi, Omid Techno-Press 2017 Advances in energy research Vol.5 No.2

This research presents an in-depth analysis of location planning of the solar-hydrogen power plants for electricity production in different cities situated in Kerman province of Iran. Ten cities were analyzed in order to select the most suitable location for the construction of a solar-hydrogen power plant utilizing photovoltaic panels. Data envelopment analysis (DEA) methodology was applied to prioritize cities for installing the solar-hydrogen power plant so that one candidate location was selected for each city. Different criteria including population, distance to main road, flood risk, wind speed, sunshine hours, air temperature, humidity, horizontal solar irradiation, dust, and land costare used for the analysis. From the analysis, it is found that among the candidates' cities, the site of Lalezar is ranked as the first priority for the solar-hydrogen system development. A measure of validity is obtained when results of the DEA method are compared with the results of the technique for ordering preference by similarity to ideal solution (TOPSIS). Applying TOPSIS model, it was found that city of Lalezar ranked first, and Rafsanjan gained last priority for installing the solar-hydrogen power plants. Cities of Baft, Sirjan, Kerman, Shahrbabak, Kahnouj, Shahdad, Bam, and Jiroft ranked second to ninth, respectively. The validity of the DEA model is compared with the results of TOPSIS and it is demonstrated that the two methods produced similar results. The solar-hydrogen power plant is considered for installation in the city of Lalezar. It is demonstrated that installation of the proposed solar-hydrogen system in Lalezar can lead to yearly yield of 129 ton-H2 which covers 4.3% of total annual energy demands of the city.

Mobile Botnet Attacks - an Emerging Threat: Classification, Review and Open Issues

( Ahmad Karim ),( Syed Adeel Ali Shah ),( Rosli Bin Salleh ),( Muhammad Arif ),( Rafidah Md Noor ),( Shahaboddin Shamshirband ) 한국인터넷정보학회 2015 KSII Transactions on Internet and Information Syst Vol.9 No.4

The rapid development of smartphone technologies have resulted in the evolution of mobile botnets. The implications of botnets have inspired attention from the academia and the industry alike, which includes vendors, investors, hackers, and researcher community. Above all, the capability of botnets is uncovered through a wide range of malicious activities, such as distributed denial of service (DDoS), theft of business information, remote access, online or click fraud, phishing, malware distribution, spam emails, and building mobile devices for the illegitimate exchange of information and materials. In this study, we investigate mobile botnet attacks by exploring attack vectors and subsequently present a well-defined thematic taxonomy. By identifying the significant parameters from the taxonomy, we compared the effects of existing mobile botnets on commercial platforms as well as open source mobile operating system platforms. The parameters for review include mobile botnet architecture, platform, target audience, vulnerabilities or loopholes, operational impact, and detection approaches. In relation to our findings, research challenges are then presented in this domain.