An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants

Özcan, Evrencan; Danışan, Tuğba; Yumuşak, Rabia; Eren, Tamer

doi:10.17531/ein.2020.3.3

Artykuł - szczegóły

Tytuł artykułu

An artificial neural network model supported with multi criteria decision making approaches for maintenance planning in hydroelectric power plants

Autorzy

Özcan Evrencan , Danışan Tuğba , Yumuşak Rabia , Eren Tamer

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.17531/ein.2020.3.3

Warianty tytułu

Planowanie utrzymania ruchu w elektrowniach wodnych w oparciu o model sztucznej sieci neuronowej wsparty wielokryterialnymi metodami podejmowania decyzji

Języki publikacji

Abstrakty

Power plants are the large-scale production facilities with the main purpose of realizing uninterrupted, reliable, efficient, economic and environmentally friendly energy generation. Maintenance is one of the critical factors in achieving these comprehensive goals, which are called as sustainable energy supply. The maintenance processes carried out in order to ensure sustainable energy supply in the power plants should be managed due to the costs arising from time requirement, the use of material and labor, and the loss of generation. In this respect, it is critical that the fault dates are forecasted, and maintenance is performed without failure in power plants consisting of thousands of equipment. In this context in this study, the maintenance planning problem for equipment with high criticality level is handled in one of the large-scale hydroelectric power plants that meet the quintile of Turkey’s energy demand as of the end of 2018. In the first stage, the evaluation criteria determined by the power plant experts are weighted by the Analytical Hierarchy Process (AHP), which is an accepted method in the literature, in order to determine the criticality levels of the equipment in terms of power plant at the next stage. In order to obtain the final priority ranking of the equipment in terms of power plant within the scope of these weights, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used because of its advantages compared to other outranking algorithms. As a result of this solution, for the 14 main equipment groups with the highest criticality level determined on the basis of the power plant, periods between two breakdowns are estimated, and maintenance planning is performed based on these periods. In the estimation phase, an artificial neural network (ANN) model has been established by using 11-years fault data for selected equipment groups and the probable fault dates are estimated by considering a production facility as a system without considering the sector for the first time in the literature. With the plan including the maintenance activities that will be carried out before the determined breakdown dates, increasing the generation efficiency, extending the economic life of the power plant, minimizing the generation costs, maximizing the plant availability rate and maximizing profit are aimed. The maintenance plan is implemented for 2 years in the power plant and the unit shutdowns resulting from the selected equipment groups are not met and the mentioned goals are reached.

Elektrownie to zakłady produkcyjne o dużej skali, których głównym celem jest nieprzerwane, niezawodne, wydajne, rentowne oraz przyjazne dla środowiska wytwarzanie energii. Utrzymanie ruchu stanowi jeden z kluczowych czynników pozwalających na osiągnięcie tych szeroko zakrojonych celów, które określa się wspólnym mianem zrównoważonych dostaw energii. W elektrowniach, procesami utrzymania ruchu, realizowanymi w celu zapewnienia zrównoważonych dostaw energii, zarządza się z uwzględnieniem kosztów związanych z wymogami czasowymi, kosztów materiałów i robocizny oraz strat wytwarzania energii. Ponieważ elektrownie wykorzystują tysiące różnych urządzeń, niezwykle ważne jest prognozowanie dat wystąpienia uszkodzeń oraz zapewnienie bezawaryjnego utrzymania ruchu. W przedstawionych badaniach, rozważano problem planowania utrzymania ruchu sprzętu o wysokim poziomie krytyczności na przykładzie jednej z dużych elektrowni wodnych, która na koniec 2018 r. pokrywała jedną piątą zapotrzebowania Turcji na energię elektryczną. W pierwszym etapie badań, kryteria oceny określone przez ekspertów zatrudnionych w elektrowni ważono za pomocą powszechnie stosowanej w literaturze metody procesu hierarchii analitycznej (AHP) w celu ustalenia poziomów krytyczności poszczególnych elementów wyposażenia elektrowni. Aby opracować ostateczny ranking priorytetowości elementów wyposażenia elektrowni na podstawie określonych wcześniej wag, zastosowano technikę TOPSIS, która polega na porządkowaniu preferencji na podstawie podobieństwa do idealnego rozwiązania. Techniki tej użyto ze względu na jej zalety, których nie mają inne algorytmy oparte na relacji przewyższania (ang. outranking algorithms). Na podstawie wyników otrzymanych dla 14 głównych grup urządzeń o najwyższym poziomie krytyczności, określonym na podstawie danych pochodzących z elektrowni, oszacowano czasy pomiędzy dwiema awariami, a na ich podstawie zaplanowano działania konserwacyjne. W fazie szacowania, opracowano model sztucznej sieci neuronowej (ANN) w oparciu o dane o uszkodzeniach, które wystąpiły w ostatnich 11 latach działania elektrowni, dla wybranych grup urządzeń. Przewidywane daty wystąpienia uszkodzeń szacowano, po raz pierwszy w literaturze, biorąc pod uwagę zakład produkcyjny jako system, bez uwzględnienia sektora produkcyjnego. Plan obejmuje działania konserwacyjne, które mają być przeprowadzone przed przewidywanymi datami awarii, w celu zwiększenia wydajności wytwarzania energii, przedłużenia żywotności elektrowni, minimalizacji kosztów wytwarzania energii, maksymalizacji wskaźnika dostępności elektrowni oraz maksymalizacji zysków. Opracowany plan konserwacji wdrażano w omawianej elektrowni przez 2 lata. W tym okresie nie odnotowano przerw w pracy jednostek wytwórczych spowodowanych awarią rozważanych grup urządzeń, co oznacza, że wspomniane cele zostały osiągnięte.

Słowa kluczowe

artificial neural networks hydroelectric power plant failure period estimation maintenance planning AHP TOPSIS

sztuczne sieci neuronowe elektrownia wodna szacowanie czasu między uszkodzeniami planowanie utrzymania ruchu AHP TOPSIS

Wydawca

Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne

Czasopismo

Eksploatacja i Niezawodność

Rocznik

2020

Tom

Vol. 22, no. 3

Strony

400--418

Opis fizyczny

Bibliogr. 92 poz., rys., tab.

Twórcy

autor

Özcan Evrencan

evrencan.ozcan@kku.edu.tr

Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey

autor

Danışan Tuğba

tugbadanisan@gmail.com

Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey

autor

Yumuşak Rabia

rabiayumusak95@gmail.com

Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey

autor

Eren Tamer

teren@kku.edu.tr

Industrial Engineering Department Kırıkkale University 71451, Kırıkkale, Turkey

Bibliografia

1. Adoghe AU, Awosope COA, Ekeh JC. Asset maintenance planning in electric power distribution network using statistical analysis of outage data. International Journal of Electrical Power & Energy Systems 2013; 47: 424-35,https://doi.org/10.1016/j.ijepes.2012.10.061.
2. Adya M, Collopy F. How effective are neural networks at forecasting and prediction? A review and evaluation. Journal of Forecasting 1998;17: 481-95, https://doi.org/10.1002/(SICI)1099-131X(1998090)17:5/6<481::AID-FOR709>3.0.CO;2-Q.
3. Al-Shayea QK. Artificial neural networks in medical diagnosis. International Journal of Computer Science Issues 2011; 8: 150-154.
4. Aly MF, El-hameed HMA. Integrating AHP and genetic algorithm model adopted for personal selection. International Journal of Engineering Trends and Technology (IJETT) 2013; 6.
5. Arıbaş M, Özcan U. Akademik araştırma projelerinin AHP ve TOPSIS yöntemleri kullanılarak değerlendirilmesi. Politeknik Dergisi 2016;19: 163-173.
6. Ayodeji A, Liu Y kuo, Xia H. Knowledge base operator support system for nuclear power plant fault diagnosis. Progress in Nuclear Energy 2018; 105: 42-50, https://doi.org/10.1016/j.pnucene.2017.12.013.
7. Bangalore P, Patriksson M. Analysis of SCADA data for early fault detection, with application to the maintenance management of wind turbines. Renewable Energy 2018; 115: 521-532, https://doi.org/10.1016/j.renene.2017.08.073.
8. Barros CP, Wanke P. An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management 2015; 44-45: 90-102, https://doi.org/10.1016/j.jairtraman.2015.03.002.
9. Behzadian M, Kazemzadeh RB, Albadvi A, Aghdasi M. PROMETHEE: A comprehensive literature review on methodologies and applications.European Journal of Operational Research 2010; 200: 198-215, https://doi.org/10.1016/j.ejor.2009.01.021.
10. Behzadian M, Khanmohammadi Otaghsara S, Yazdani M, Ignatius J. A state-of the-art survey of TOPSIS applications. Expert Systems with Applications 2012; 39: 13051-13069, https://doi.org/10.1016/j.eswa.2012.05.056.
11. Bevilacqua M, Braglia M. Analytic hierarchy process applied to maintenance strategy selection. Reliability Engineering & System Safety 2000; 70: 71-83, https://doi.org/10.1016/S0951-8320(00)00047-8.
12. Bi R, Zhou C, Hepburn DM. Detection and classification of faults in pitch-regulated wind turbine generators using normal behaviour models based on performance curves. Renewable Energy 2017; 105: 674-688, https://doi.org/10.1016/j.renene.2016.12.075.
13. Box GEP, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis: forecasting and control. John Wiley & Sons; 2015.
14. Braglia M, Carmignani G, Frosolini M, Zammori F. Data classification and MTBF prediction with a multivariate analysis approach. Reliability Engineering & System Safety 2012; 97: 27-35, https://doi.org/10.1016/j.ress.2011.09.010.
15. Chang P-L, Chen Y-C. A fuzzy multi-criteria decision making method for technology transfer strategy selection in biotechnology. Fuzzy Sets and Systems 1994; 63: 131-139, https://doi.org/10.1016/0165-0114(94)90344-1.
16. Chen B, Matthews PC, Tavner PJ. Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS. Expert Systems with Applications 2013; 40: 6863-6876, https://doi.org/10.1016/j.eswa.2013.06.018.
17. Chen D, Fang Z, Wu L, Zhu X. Study on the Prediction of Complex Equipment El MTBF DGMW (p/q)(1, 1) Model Based on the Small Sample. Journal of Grey System 2013; 25.
18. Cheng B, Titterington DM. Neural networks: A review from a statistical perspective. Statistical Science 1994: 2-30, https://doi.org/10.1214/ss/1177010638.
19. Çamkoru AM, Sayın VO. Bakım maliyeti yönetimi, Mühendis ve Makine 2012; 53(635): 16-20.
20. Dougherty M. A review of neural networks applied to transport. Transportation Research Part C: Emerging Technologies 1995; 3: 247-260,https://doi.org/10.1016/0968-090X(95)00009-8.
21. Energy statistics. UCTEA. The Chamber of Electrical Engineers (www.emo.org.tr/ekler/5aa1f79e3852d94_ek.pdf).
22. Fernandez C, Soria E, Martin JD, Serrano AJ. Neural networks for animal science applications: Two case studies. Expert Systems with Applications 2006; 31:444-450, https://doi.org/10.1016/j.eswa.2005.09.086.
23. Gardner MW, Dorling SR. Artificial neural networks (the multilayer perceptron)-a review of applications in the atmospheric sciences. Atmospheric Environment 1998; 32: 2627-2636, https://doi.org/10.1016/S1352-2310(97)00447-0.
24. Govindan K, Jepsen MB. ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research 2016; 250: 1-29, https://doi.org/10.1016/j.ejor.2015.07.019.
25. Granger CWJ, Terasvirta T. Modelling non-linear economic relationships. OUP Cat 1993.
26. Gul M, Celik E, Aydin N, Taskin Gumus A, Guneri AF. A state of the art literature review of VIKOR and its fuzzy extensions on applications. Applied Soft Computing 2016; 46: 60-89, https://doi.org/10.1016/j.asoc.2016.04.040.
27. Hajian A, Styles P. Application of soft computing and intelligent methods in geophysics. Springer International Publishing 2018, https://doi.org/10.1007/978-3-319-66532-0.
28. Haykin S. Neural networks: a comprehensive foundation. Prentice Hall PTR; 1994.
29. Hebb DO. The Organizations of Behavior: a Neuropsychological Theory. Lawrence Erlbaum; 1963.
30. Hebbs DG. The organization of behavior. Wiely Sons, New York, NY, USA 1949.
31. Hwang C-L, Yoon K. Methods for multiple attribute decision making. Multiple Attribute Decision Making 1981: 58-191, https://doi.org/10.1007/978-3-642-48318-9_3.
32. Illias HA, Chai XR, Bakar AHA, Mokhlis H. Transformer incipient fault prediction using combined artificial neural network and various particle swarm optimisation techniques. PLoS One 2015; 10: 1-17, https://doi.org/10.1371/journal.pone.0129363.
33. Ishizaka A, Labib A. Review of the main developments in the analytic hierarchy process. Expert Systems with Applications 2011; 38: 14336-14345, https://doi.org/10.1016/j.eswa.2011.04.143.
34. Jiang R, Huang R, Huang C. Modeling the effect of environmental conditions on reliability of wind turbines. Journal of Shanghai Jiaotong University (Science) 2016; 21: 462-466, https://doi.org/10.1007/s12204-016-1747-7.
35. Jones B, Jenkinson I, Yang Z, Wang J. The use of Bayesian network modelling for maintenance planning in a manufacturing industry. Reliability Engineering & System Safety 2010; 95: 267-277, https://doi.org/10.1016/j.ress.2009.10.007.
36. Kahraman C, Ruan D, Doǧan I. Fuzzy group decision-making for facility location selection. Information Sciences 2003; 157: 135-153,https://doi.org/10.1016/S0020-0255(03)00183-X.
37. Kalogirou SA. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews 2001; 5: 373-401, https://doi.org/10.1016/S1364-0321(01)00006-5.
38. Komal S, Sharma SP. Fuzzy reliability analysis of repairable industrial systems using soft-computing based hybridized techniques. Applied Soft Computing 2014; 24: 264-276, https://doi.org/10.1016/j.asoc.2014.06.054.
39. Kubler S, Robert J, Derigent W, Voisin A, Le Traon Y. A state-of the-art survey & testbed of fuzzy AHP (FAHP) applications. Expert Systems with Applications 2016; 65: 398-422, https://doi.org/10.1016/j.eswa.2016.08.064.
40. Kumar A, Sah B, Singh AR, Deng Y, He X, Kumar P, et al. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews 2017; 69: 596-609, https://doi.org/10.1016/j.rser.2016.11.191.
41. Kumar R, Singal SK. Penstock material selection in small hydropower plants using MADM methods. Renewable and Sustainable Energy Reviews 2015; 52: 240-255, https://doi.org/10.1016/j.rser.2015.07.018.
42. Kuo RJ, Chi SC, Kao SS. A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network. Computers in Industry 2002; 47: 199-214, https://doi.org/10.1016/S0166-3615(01)00147-6.
43. Kusiak A, Li W. The prediction and diagnosis of wind turbine faults. Renewable Energy 2011; 36:16-23, https://doi.org/10.1016/j.renene.2010.05.014.
44. Lee JW, Kim SH. Using analytic network process and goal programming for interdependent information system project selection. Computers & Operations Research 2000; 27: 367-382, https://doi.org/10.1016/S0305-0548(99)00057-X.
45. Liang R-H, Hsu Y-Y. Scheduling of hydroelectric generations using artificial neural networks. IEE Proceedings-Generation, Transmission and Distribution 1994;141: 452-458, https://doi.org/10.1049/ip-gtd:19941156.
46. Liao S-H, Wen C-H. Artificial neural networks classification and clustering of methodologies and applications-literature analysis from 1995 to 2005 Expert Systems with Applications 2007; 32: 1-11, https://doi.org/10.1016/j.eswa.2005.11.014.
47. Liberatore MJ, Nydick RL. The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research 2008; 189: 194-207, https://doi.org/10.1016/j.ejor.2007.05.001.
48. Liu Y, Fan J, Li Y. One system reliability assessment method for CNC grynder. Eksploatacja i Niezawodnosc - Maintenancen and Reliability 2014; 16(1): 97-104.
49. Lu Y, Sun L, Zhang X, Feng F, Kang J, Fu G. Condition based maintenance optimization for offshore wind turbine considering opportunities based on neural network approach. Applied Ocean Research 2018; 74: 69-79, https://doi.org/10.1016/j.apor.2018.02.016.
50. Mardani A, Zavadskas EK, Khalifah Z, Zakuan N, Jusoh A, Nor KM, et al. A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015. Renewable and Sustainable Energy Reviews 2017; 71: 216-256, https://doi.org/10.1016/j.rser.2016.12.053.
51. Mayadevi N, Vinodchandra S S, Ushakumari S. A review on expert system applications in power plants. International Journal of Electrical and Computer Engineering 2014; 4(1): 116.
52. McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943; 5: 115-133, https://doi.org/10.1007/BF02478259.
53. Messai A, Mellit A, Abdellani I, Massi Pavan A. On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs. Progress in Nuclear Energy 2015; 79: 8-21, https://doi.org/10.1016/j.pnucene.2014.10.013.
54. Minsky M, Paper S.Perceptrons. MIT Press, Cambridge MA, USA 1969.
55. Mohd Ali J, Hussain MA, Tade MO, Zhang J. Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey. Expert Systems with Applications 2015; 42: 5915-5931, https://doi.org/10.1016/j.eswa.2015.03.023.
56. Nasiri S, Khosravani MR, Weinberg K. Fracture mechanics and mechanical fault detection by artificial intelligence methods: A review. Engineering Failure Analysis 2017; 81: 270-293, https://doi.org/10.1016/j.engfailanal.2017.07.011.
57. Polo FAO, Ferrero Bermejo J, Gómez Fernández JF, Crespo Márquez A. Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models. Renewable Energy 2015;81: 227-238, https://doi.org/10.1016/j.renene.2015.03.023.
58. Özcan EC, Ünlüsoy S, Eren T. A combined goal programming - AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable and Sustainable Energy Reviews 2017;78: 1410-1423, https://doi.org/10.1016/j.rser.2017.04.039.
59. Pankratz A. Forecasting with univariate Box-Jenkins models: Concepts and cases. vol. 224. John Wiley & Sons; 2009.
60. Ramadevi R, Sheela Rani B, Prakash V. Role of hidden neurons in an elman recurrent neural network in classification of cavitation signals. Int J Comput Appl 2012; 37(7): 9-13, https://doi.org/10.5120/4618-6626.
61. Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep learning for health informatics. IEEE journal of biomedical and health informatics 2016; 21(1): 4-21, https://doi.org/10.1109/JBHI.2016.2636665.
62. Ripley BD. Statistical aspects of neural networks. Networks and Chaos-Statistical and Probabilistic Aspects 1993; 50: 40-123, https://doi.org/10.1007/978-1-4899-3099-6_2.
63. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological Review 1958; 65:386, https://doi.org/10.1037/h0042519.
64. Rumelhart DE, Hinton GE, Williams RJ. Learning internal representation by backpropagating errors. DE Rumelhart, & JL McCleland Parallel Distributed Processing: Explorations in the Microstructure of Cognition 1986; 1,https://doi.org/10.7551/mitpress/5236.001.0001.
65. Saaty TL. The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill International Book Company; 1980.
66. San Cristóbal JR. Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method. Renewable Energy 2011; 36: 498-502, https://doi.org/10.1016/j.renene.2010.07.031.
67. Schlechtingen M, Ferreira Santos I. Comparative analysis of neural network and regression based condition monitoring approaches for wind turbine fault detection. Mechanical Systems and Signal Processing 2011; 25:1849-1875, https://doi.org/10.1016/j.ymssp.2010.12.007.
68. Schlechtingen M, Santos IF. Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples. Applied Soft Computing 2014; 14: 447-460, https://doi.org/10.1016/j.asoc.2013.09.016.
69. Shankaracharya DO, Samanta S, Vidyarthi AS. Computational intelligence in early diabetes diagnosis: A review. The Review of Diabetic Studies: RDS 2010; 7: 252-261, https://doi.org/10.1900/RDS.2010.7.252.
70. Sharda R. Neural networks for the MS/OR analyst: An application bibliography. Interfaces (Providence) 1994; 24: 116-130, https://doi.org/10.1287/inte.24.2.116.
71. Sheela K G. Deepa S. N. Review on methods to fix number of hidden neurons in neural networks. Mathematical Problems in Engineering 2013, https://doi.org/10.1155/2013/425740
72. Sindhu S, Nehra V, Luthra S. Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renewable and Sustainable Energy Reviews 2017; 73: 496-511, https://doi.org/10.1016/j.rser.2017.01.135.
73. Sipahi S, Timor M. The analytic hierarchy process and analytic network process: an overview of applications. Management Decision 2010; 48: 775-808, https://doi.org/10.1108/00251741011043920.
74. Suganthi L, Samuel AA. Energy models for demand forecasting - A review. Renewable and Sustainable Energy Reviews 2012; 16:1223-1240, https://doi.org/10.1016/j.rser.2011.08.014.
75. Sun P, Li J, Wang C, Lei X. A generalized model for wind turbine anomaly identification based on SCADA data. Applied Energy 2016; 168:550-567, https://doi.org/10.1016/j.apenergy.2016.01.133.
76. Taha Z, Rostam S. A fuzzy AHP-ANN-based decision support system for machine tool selection in a flexible manufacturing cell. The International Journal of Advanced Manufacturing Technology 2011; 57: 719-733, https://doi.org/10.1007/s00170-011-3323-5.
77. Vaidya OS, Kumar S. Analytic hierarchy process: An overview of applications. European Journal of Operational Research 2006; 169: 1-29, https://doi.org/10.1016/j.ejor.2004.04.028.
78. Vedachalam N, Ramadass GA. Reliability assessment of multi-megawatt capacity offshore dynamic positioning systems. Applied Ocean Research 2017; 63: 251-261, https://doi.org/10.1016/j.apor.2017.02.001.
79. Velasquez M, Hester PT. An analysis of multi-criteria decision making methods. International Journal of Operations Research 2013;10: 56-66.
80. Voyant C, Notton G, Kalogirou S, Nivet M-L, Paoli C, Motte F, et al. Machine learning methods for solar radiation forecasting: A review. Renewable Energy 2017; 105: 569-582, https://doi.org/10.1016/j.renene.2016.12.095.
81. Wang G, Huang SH, Dismukes JP. Product-driven supply chain selection using integrated multi-criteria decision-making methodology. International Journal of Production Economics 2004; 91: 1-15, https://doi.org/10.1016/S0925-5273(03)00221-4.
82. Wang JJ, Jing YY, Zhang CF, Zhao JH. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews 2009; 13: 2263-2278, https://doi.org/10.1016/j.rser.2009.06.021.
83. Wang Z, Srinivasan RS. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews 2017; 75: 796-808, https://doi.org/10.1016/j.rser.2016.10.079.
84. Wanke P, Azad MAK, Barros CP, Hassan MK. Predicting efficiency in Islamic banks: An integrated multicriteria decision making (MCDM) approach. Journal of International Financial Markets, Institutions and Money 2016; 45: 126-141, https://doi.org/10.1016/j. intfin.2016.07.004.
85. Weron R. Electricity price forecasting: A review of the state-of-the-art with a look into the future. International Journal of Forecasting 2014; 30: 1030-1081, https://doi.org/10.1016/j.ijforecast.2014.08.008.
86. White H. Learning in artificial neural networks: A statistical perspective. Neural Computation 1989; 1: 425-464, https://doi.org/10.1162/neco.1989.1.4.425.
87. Wong BK, Selvi Y. Neural network applications in finance: a review and analysis of literature (1990-1996). Information & Management 1998; 34:129-139, https://doi.org/10.1016/S0378-7206(98)00050-0.
88. Yang Y, Lu Z, Luo X, Ge Z, Qian Y. Mean failure mass and mean failure repair time: parameters linking reliability, maintainability and supportability. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2014;16(2):307-312.
89. Zhang G, Patuwo BE, Hu MY. Forecasting with artificial neural networks:: The state of the art. International Journal of Forecasting 1998;14: 35-62, https://doi.org/10.1016/S0169-2070(97)00044-7.
90. Zhang GP. Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Cybern Part C (Applications and Reviews) 2000; 30: 451-462, https://doi.org/10.1109/5326.897072.
91. Zhang Y, Ding X, Liu Y, Griffin PJ. An artificial neural network approach to transformer fault diagnosis. IEEE Transactions on Power Delivery 1996; 16: 55, https://doi.org/10.1109/MPER.1996.4311026.
92. Zyoud SH, Fuchs-Hanusch D. A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications 2017; 78:158-181, https://doi.org/10.1016/j.eswa.2017.02.016.

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-ca7c56f2-84d8-4f1d-bbd0-52fe3a450db9