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Analyzing wind power data using analysis of means under neutrosophic statistics

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Abstract

In this paper, the analysis of means test under the neutrosophic statistics is presented. The necessary procedure is given to apply the proposed neutrosophic analysis of means. The proposed neutrosophic analysis of means is explained with the aid of wind power data. The efficiency of the proposed neutrosophic analysis of means is compared with existing analysis of means under classical statistics. From the comparison, we conclude that the proposed neutrosophic analysis of means is quite effective, informative and flexible than the existing analysis of means to be applied in the area of renewable energy.

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References

  • Abdel-Baset M, Chang V, Gamal A (2019) Evaluation of the green supply chain management practices: a novel neutrosophic approach. Comput Ind 108:210–220

    Article  Google Scholar 

  • Abdel-Basset M, Mohamed M, Elhoseny M, Chiclana F, Zaied AE-NH (2019a) Cosine similarity measures of bipolar neutrosophic set for diagnosis of bipolar disorder diseases. Artif Intell Med 101:101735

    Article  Google Scholar 

  • Abdel-Basset M, Nabeeh NA, El-Ghareeb HA, Aboelfetouh A (2019) Utilising neutrosophic theory to solve transition difficulties of IoT-based enterprises. Enterprise Inf Syst 1–21

  • Akpinar EK, Akpinar S (2005) A statistical analysis of wind speed data used in installation of wind energy conversion systems. Energy Convers Manag 46(4):515–532

    Article  Google Scholar 

  • Amin F, Fahmi A (2019) Human Immunodeficiency Virus (HIV) infection model based on triangular neutrosophic cubic hesitant fuzzy number. Int J Biomath 12(05):1950055

    Article  Google Scholar 

  • Amin F, Fahmi A, Abdullah S (2019) Dealer using a new trapezoidal cubic hesitant fuzzy TOPSIS method and application to group decision-making program. Soft Comput 23(14):5353–5366

    Article  MATH  Google Scholar 

  • Amin F, Fahmi A, Abdullah S, Ali A, Ahmad R, Ghani F (2018) Triangular cubic linguistic hesitant fuzzy aggregation operators and their application in group decision making. J Intell Fuzzy Syst 34(4):2401–2416

    Article  Google Scholar 

  • Aslam M (2018) A new sampling plan using neutrosophic process loss consideration. Symmetry 10(5):132

    Article  Google Scholar 

  • Aslam M (2019) Neutrosophic analysis of variance: application to university students. Complex Intell Syst 1–5

  • Aslam M (2020) Design of the Bartlett and Hartley tests for homogeneity of variances under indeterminacy environment. J Taibah Univ Sci 14(1):6–10

    Article  Google Scholar 

  • Aslam M, Albassam M (2019) Application of neutrosophic logic to evaluate correlation between prostate cancer mortality and dietary fat assumption. Symmetry 11(3):330

    Article  Google Scholar 

  • Azad K, Rasul M, Halder P, Sutariya J (2019) Assessment of wind energy prospect by Weibull distribution for prospective wind sites in Australia. Energy Procedia 160:348–355

    Article  Google Scholar 

  • Bidaoui H, El Abbassi I, El Bouardi A, Darcherif A (2019) Wind speed data analysis using Weibull and Rayleigh distribution functions, case study: five cities Northern Morocco. ProcediaManuf 32:786–793

    Google Scholar 

  • Brereton RG (2019) Introduction to analysis of variance. J Chemom 33(1):e3018

    Article  Google Scholar 

  • Broumi S, Bakali A, Talea M, Smarandache F (2018) Bipolar neutrosophic minimum spanning tree. Infinite Study.

  • Broumi S, Smarandache F (2013) Correlation coefficient of interval neutrosophic set. Paper presented at the applied mechanics and materials

  • Chauhan A, Saini R (2014) Statistical analysis of wind speed data using Weibull distribution parameters. Paper presented at the 2014 1st international conference on non conventional energy (ICONCE 2014)

  • Chen J, Ye J, Du S (2017) Scale effect and anisotropy analyzed for neutrosophic numbers of rock joint roughness coefficient based on neutrosophic statistics. Symmetry 9(10):208

    Article  Google Scholar 

  • Chen J, Ye J, Du S, Yong R (2017) Expressions of rock joint roughness coefficient using neutrosophic interval statistical numbers. Symmetry 9(7):123

    Article  Google Scholar 

  • Choi Y, Lee H, Irani Z (2018) Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Ann Oper Res 270(1–2):75–104

    Article  MathSciNet  Google Scholar 

  • Fahmi A, Abdullah S, Amin F (2019) Cubic uncertain linguistic powered Einstein aggregation operators and their application to multi-attribute group decision making. Math Sci 13(2):129–152

    Article  MathSciNet  MATH  Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Ali A (2017) Precursor selection for sol–gel synthesis of titanium carbide nanopowders by a new cubic fuzzy multi-attribute group decision-making model. J IntellSyst 28(5):699–720

    Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Ali A, Ahmed R, Shakeel M (2019) Trapezoidal cubic hesitant fuzzy aggregation operators and their application in group decision-making. J Intell Fuzzy Syst 36(4):3619–3635

    Article  Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Ali A, Khan WA (2018) Some geometric operators with triangular cubic linguistic hesitant fuzzy number and their application in group decision-making. J Intell Fuzzy Syst 35(2):2485–2499

    Article  Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Aslam M, Hussain S (2019) Trapezoidal linguistic cubic fuzzy TOPSIS method and application in a group decision making program. J IntellSyst 29(1):1283–1300

    Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Khan MSA (2019) Trapezoidal cubic fuzzy number Einstein hybrid weighted averaging operators and its application to decision making. Soft Comput 23(14):5753–5783

    Article  MATH  Google Scholar 

  • Fahmi A, Abdullah S, Amin F, Siddiqui N, Ali A (2017) Aggregation operators on triangular cubic fuzzy numbers and its application to multi-criteria decision making problems. J Intell Fuzzy Syst 33(6):3323–3337

    Article  Google Scholar 

  • Fahmi A, Amin F, Abdullah S, Ali A (2018) Cubic fuzzy Einstein aggregation operators and its application to decision-making. Int J Syst Sci 49(11):2385–2397

    Article  MathSciNet  Google Scholar 

  • Fahmi A, Amin F, Khan M, Smarandache F (2019) Group decision making based on triangular neutrosophic cubic fuzzy einstein hybrid weighted averaging operators. Symmetry 11(2):180

    Article  MATH  Google Scholar 

  • Fahmi A, Amin F, Smarandache F, Khan M, Hassan N (2018) Triangular cubic hesitant fuzzy Einstein hybrid weighted averaging operator and its application to decision making. Symmetry 10(11):658

    Article  MATH  Google Scholar 

  • Guo Y, Sengur A (2015) NCM: Neutrosophic c-means clustering algorithm. Pattern Recogn 48(8):2710–2724

    Article  Google Scholar 

  • Guo Y, Sengur A (2015) NECM: Neutrosophic evidential c-means clustering algorithm. Neural Comput Appl 26(3):561–571

    Article  Google Scholar 

  • Guo Y, Şengür A, Tian J-W (2016) A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set. Comput Methods Programs Biomed 123:43–53

    Article  Google Scholar 

  • Hanafy I, Salama A, Mahfouz M (2013) Correlation coefficients of neutrosophic sets by centroid method. Infinite Study

  • Harper JF (1984) Peritz’F test: basic program of a robust multiple comparison test for statistical analysis of all differences among group means. Comput Biol Med 14(4):437–445

    Article  Google Scholar 

  • Kacprzyk J, Szmidt E, Zadrożny S, Atanassov KT, Krawczak M (2017) Advances in fuzzy logic and technology 2017: proceedings of: EUSFLAT-2017-The 10th conference of the European society for fuzzy logic and technology, 11–15 Sept 2017, Warsaw, Poland IWIFSGN’2017–The 16th international workshop on intuitionistic fuzzy sets and generalized nets, 13–15 Sept 2017, Warsaw, Poland, vol 2. Springer.

  • Katinas V, Gecevicius G, Marciukaitis M (2018) An investigation of wind power density distribution at location with low and high wind speeds using statistical model. Appl Energy 218:442–451

    Article  Google Scholar 

  • Lee C, Liu C, Mehrotra S, Shahidehpour M (2013) Modeling transmission line constraints in two-stage robust unit commitment problem. IEEE Trans Power Syst 29(3):1221–1231

    Article  Google Scholar 

  • Lee W-J, Jung H-Y, Yoon JH, Choi SH (2017) Analysis of variance for fuzzy data based on permutation method. Int J Fuzzy Logic IntellSyst 17(1):43–50

    Article  Google Scholar 

  • Liu C, Botterud A, Zhou Z, Du P (2016) Fuzzy energy and reserve co-optimization with high penetration of renewable energy. IEEE Trans Sustain Energy 8(2):782–791

    Article  Google Scholar 

  • Mahmood FH, Resen AK, Khamees AB (2019) Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq. Energy reports.

  • Min Y, Chen Y, Yang H (2019) A statistical modeling approach on the performance prediction of indirect evaporative cooling energy recovery systems. Appl Energy 255:113832

    Article  Google Scholar 

  • Mishra P, Singh U, Pandey CM, Mishra P, Pandey G (2019) Application of student’s t-test, analysis of variance, and covariance. Ann Cardiac Anaesthesia 22(4):407

    Article  Google Scholar 

  • Moewes C, Mikut R, Kruse R (2015) Fuzzy control Springer handbook of computational intelligence. Springer, pp 269–283

  • Mohammed D, Abdelaziz M, Sidi A, Mohammed E, Elmostapha E (2019) Wind speed data and wind energy potential using weibull distribution in Zagora, Morocco. Int J Renew Energy Dev 8(3).

  • Mohanty V, AnnanNaidu P (2013) Fraud detection using outlier analysis: a survey. Int J Eng Sci Res Technol 2(6).

  • Montenegro M, Casals MAR, Lubiano MAA, Gil MAA (2001) Two-sample hypothesis tests of means of a fuzzy random variable. Inf Sci 133(1–2):89–100

    Article  MathSciNet  MATH  Google Scholar 

  • Moradnezhadi YM (2014) Determination of a some simple methods for outlier detection in maximum daily rainfall (case study: Baliglichay Watershed Basin-Ardebil Province–Iran). Bull Env Pharmacol Life Sci 3(3):110–117

    Google Scholar 

  • Nabeeh NA, Smarandache F, Abdel-Basset M, El-Ghareeb HA, Aboelfetouh A (2019) An integrated neutrosophic-topsis approach and its application to personnel selection: a new trend in brain processing and analysis. IEEE Access 7:29734–29744

    Article  Google Scholar 

  • Patro S, Smarandache F (2016) The neutrosophic statistical distribution, more problems, more solutions. Infinite Study.

  • Peng X, Dai J (2018) Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Comput Appl 29(10):939–954

    Article  Google Scholar 

  • Ramig PF (1983) Applications of the analysis of means. J Quality Technol 15(1):19–25

    Article  Google Scholar 

  • Smarandache F (1998) Neutrosophy. Neutrosophic probability, set, and logic, proquest information & learning, vol 105. Ann Arbor, Michigan, USA, pp 118–123.

  • Smarandache F (2014) Introduction to neutrosophic statistics. Infinite Study

  • Smarandache F, Khalid HE (2015) Neutrosophic precalculus and neutrosophic calculus. Infinite Study

  • Taheri SM, Hesamian G (2017) Non-parametric statistical tests for fuzzy observations: fuzzy test statistic approach. Int J Fuzzy Logic Intell Syst 17(3):145–153

    Article  Google Scholar 

  • Tukey JW (1949) Comparing individual means in the analysis of variance. Biometrics 5(2):99–114

    Article  MathSciNet  Google Scholar 

  • Van Cutsem B, Gath I (1993) Detection of outliers and robust estimation using fuzzy clustering. Comput Stat Data Anal 15(1):47–61

    Article  MATH  Google Scholar 

  • Vogt M, Marten F, Braun M (2018) A survey and statistical analysis of smart grid co-simulations. Appl Energy 222:67–78

    Article  Google Scholar 

  • Xing Y, Lv C, Cao D, Lu C (2020) Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling. Appl Energy 261(1):114471

    Article  Google Scholar 

  • Zhao Y, Liu P, Wang Z, Zhang L, Hong J (2017) Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Appl Energy 207:354–362

    Article  Google Scholar 

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Acknowledgements

We are thankful to the editor and reviewers for their valuable suggestions to improve the quality of the paper.

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  1. Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, 21551, Saudi Arabia

    Muhammad Aslam

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  1. Muhammad Aslam
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Correspondence to Muhammad Aslam.

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Aslam, M. Analyzing wind power data using analysis of means under neutrosophic statistics. Soft Comput 25, 7087–7093 (2021). https://doi.org/10.1007/s00500-021-05661-0

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