Classification by Artificial Neural Network according to the values affecting Electricity Generation
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Abstract
Predicting the amount of electricity produced in a power plant is very important for today’s economy. Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, Feeding Water Tank data affect the electricity production. In this article, Etikrom A.Ş. The electricity production amount to be produced in Elazığ Etikrom A.Ş. was estimated by using the data of Oven Power (MW), Water Inlet Gas Temperature, Steam Vapor Volume, ID-Fan Speed, Feeding Water Tank data. Electricity generation amount is used as verification data. That is, by the k-means clustering method, the electricity generation amount is divided into 3 classes (low, medium, and high). 3621 data including Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, and Feeding Water Tank data were used after class 3 separation. With the K-means clustering method, 2742 of these data were clustered as low electricity, 296 as medium electricity and 583 as high electricity. This clustered data was given to the Artifical Neural Network classifier. The success rate obtained as a result of this classification is 85.81%. Classified data were analyzed by ROC curve.
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Üstüntaş T, Şahin AD (2008) Wind turbine power curve estimation based on cluster center fuzzy logic modeling. Journal of Wind Engineering and Industrial Aerodynamics 96: 611-620. Link: https://goo.gl/8xnNus
Agrawal S, Panigrahi BK, Tiwari MK (2008) Multiobjective particle swarm algorithm with fuzzy clustering for electrical power dispatch. IEEE Transactions on Evolutionary Computation 12: 529-541. Link: https://goo.gl/XXzvLA
Grubb M, Butler L, Twomey P (2006) Diversity and security in UK electricity generation: The influence of low-carbon objectives. Energy Policy 34: 4050-4062. Link: https://goo.gl/8UC56a
Aelterman P, Versichele M, Marzorati M, Boon N, Verstraete W (2008) Loading rate and external resistance control the electricity generation of microbial fuel cells with different three-dimensional anodes. Bioresource Technology 99: 8895-8902. Link: https://goo.gl/ZUpQQM
Seabra JEA, Tao L, Chum HL, Macedo IC (2010) A techno-economic evaluation of the effects of centralized cellulosic ethanol and co-products refinery options with sugarcane mill clustering. Biomass and Bioenergy 34: 1065-1078. Link: https://goo.gl/aKor6f
Dastrup SR, Zivin JG, Costa DL, Kahn ME (2012) Understanding the Solar Home price premium: Electricity generation and “Green” social status. European Economic Review 56: 961-973. Link: https://goo.gl/TwcHEU
Tekiner H, Coit DW, Felder FA (2010) Multi-period multi-objective electricity generation expansion planning problem with Monte-Carlo simulation. Electric Power Systems Research 80: 1394-1405. Link: https://goo.gl/CDQrSW
Marnay C, Venkataramanan G (2006) Microgrids in the evolving electricity generation and delivery infrastructure. In Power Engineering Society General Meeting IEEE. Link: https://goo.gl/Qe573w
kmeans. Link: https://goo.gl/my1Rrv
Arthur D, Vassilvitskii S (2007) K-means++: The Advantages of Careful Seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms p. 1027–1035. Link: https://goo.gl/iom3qT
Lloyd S (1982) Least Squares Quantization in PCM. IEEE Transactions on Information Theory 28: 129–137. Link: https://goo.gl/jy21tR
Seber GAF (1984) Multivariate Observations. Hoboken, NJ: John Wiley & Sons, Inc
Spath H (1985) Cluster Dissection and Analysis: Theory, FORTRAN Programs, Examples. Translated by Goldschmidt J. New York: Halsted Press.
Korkmaz SA, Poyraz M (2014) A New Method Based for Diagnosis of Breast Cancer Cells from Microscopic Images: DWEE—JHT. Journal of Medical Systems 38: 92. Link: https://goo.gl/2XsEHQ
Korkmaz SA, Binol H (2017) Analysis of Molecular Structure Images by using ANN, RF, LBP, HOG, and Size Reduction Methods for early Stomach Cancer Detection. Journal of Molecular Structure 1156: 255-263. Link: https://goo.gl/Po4Dkq
Korkmaz SA, Binol H, Akcicek A, Korkmaz MF (2017) A expert system for stomach cancer images with artificial neural network by using HOG features and linear discriminant analysis: HOG_LDA_ANN. Intelligent Systems and Informatics (SISY), IEEE 15th International Symposium on 327-332. Link: https://goo.gl/b1p5hh
Turkoglu I (2007) Hardware implementation of varicap diode's ANN model using PIC microcontrollers. Sensors and Actuators A: Physical 138: 288-293. Link: https://goo.gl/a13cey
Korkmaz SA (2018) LBP Özelliklerine Dayanan Lokasyon Koruyan Projeksiyon (LPP) Boyut Azaltma Metodunun Farklı Sınıflandırıcılar Üzerindeki Performanslarının Karşılaştırılması. Sakarya University Journal of Science 22: 1. Link: https://goo.gl/7ZYRNi
Korkmaz SA, Poyraz M (2014) A New Method Based for Diagnosis of Breast Cancer Cells from Microscopic Images: DWEE--JHT. Journal of Medical Systems 38: 1. Link: https://goo.gl/B8jeFc
Korkmaz SA, Poyraz M (2015) Least square support vector machine and minumum redundacy maximum relavance for diagnosis of breast cancer from breast microscopic images. Procedia-Social and Behavioral Sciences 174: 4026-4031. Link: https://goo.gl/EfrAV5
Korkmaz SA, Korkmaz MF (2015) A new method based cancer detection in mammogram textures by finding feature weights and using Kullback–Leibler measure with kernel estimation. Optik-International Journal for Light and Electron Optics 126: 2576-2583. Link: https://goo.gl/JhDKCS
Korkmaz SA, Korkmaz MF, Poyraz M (2015) Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation. Medical & biological engineering & computing 54: 561-573. Link: https://goo.gl/6WBXMs
Korkmaz SA, Eren H (2013) Cancer detection in mammograms estimating feature weights via Kullback-Leibler measure. In: Image and Signal Processing (CISP). 6th International Congress on. IEEE 2:1035-1040. Link: https://goo.gl/ukfiFj
Korkmaz SA (2017) Detecting cells using image segmentation of the cervical cancer images taken from scanning electron microscope. The Online Journal of Science and Technology 7: Link: https://goo.gl/WS6DqS
Korkmaz SA, Akcicek A, Binol H, Korkmaz MF (2017) Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features. Intelligent Systems and Informatics (SISY), IEEE 15th International Symposium on 339-342. Link: https://goo.gl/2JzbW3
Korkmaz SA, Korkmaz MF, Poyraz M, Yauphanoglu F (2016) Diagnosis of breast cancer nano-biomechanics images taken from atomic force microscope. Journal of Nanoelectronics and Optoelectronics 11: 551-559. Link: https://goo.gl/EZzDii
Sengur A (2012) Support vector machine ensembles for intelligent diagnosis of valvular heart disease. Journal of medical systems 36: 2649-2655. Link: https://goo.gl/eNzFZH
Şengür A (2008) An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnostic of valvular heart diseases. Computers in Biology and Medicine 38: 329 - 338. Link: https://goo.gl/Zt4mTu