Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model

Ashaf Mohammed Abusıda; Seçil Karatay; Rezvan Rezaeizadeh; Aybaba Hançerlioğulları

doi:10.2339/politeknik.1368126

Araştırma Makalesi

Weka Modeli Kullanılarak Libya Elektrik Şirketinin Kayıp Enerji Verilerinde Enerji Tassarufu İçin Sınıflandırma Algoritmalarının İyileştirilmesi

Yıl 2023, Cilt: 26 Sayı: 4, 1697 - 1703, 01.12.2023

Ashaf Mohammed Abusıda Seçil Karatay Rezvan Rezaeizadeh Aybaba Hançerlioğulları

https://doi.org/10.2339/politeknik.1368126

Öz

Bu çalışmanın temel amacı, Libya Genel Elektrik Şirketi'nin (GECOL-General Electricity Company of Libya) Denetleyici Kontrol ve Veri Toplama (SCADA-Supervisory Control and Data Acquisition) sisteminin veri tabanına uygulanan sınıflandırma algoritmalarının performansını karşılaştırmaktır. Şirketin yıllık enerji ve malzeme kayıpları, Libya hükümetinin araştırma alanı açısından ciddi önem taşımaktadır. Bu kayıpları en aza indirgemek için WEKA aracı olarak bilinen köklü veri madenciliği ve sınıflandırma yazılım paketi kullanılmıştır. Algoritmalar için gerekli veri girişi olarak; güç üretim büyüklüğü, enerji üretim süresi, enerji üretim tarihi, ortam sıcaklığı, nem seviyesi ve rüzgâr hızı olmak üzere altı farklı parametre göz önünde tutulmuştur. Bu çalışma ilk kez detaylı olarak, bu makalede incelenmiştir. Bununla birlikte sıcaklık değişkenlerine göre ayrıca ortamın nem, rüzgâr ve diğer atmosferik parametreler de dikkate alınarak, şirketin ve ülkenin enerji kayıpları en az seviyeye indirgenmiştir. Sonuç olarak yapılan benzetimlerle, firmanın yıllık elektrik tüketimi düşük, orta veya yüksek tüketim olarak sınıflandırılmıştır. Böylelikle, enerji tüketiminin yüksek olduğu durumlarda, enerji tüketimine ilişkin zaman dilimlerinin belirlenmesi ve sınıflandırılması konusunda doğru ve hızlı kararlar alınmasına imkan sağlanmıştır.

Anahtar Kelimeler

Gecol, Veri madenciliği, Sınıflandırma, Gecol, Scada, Weka

Kaynakça

[1] Bahssas D.M., AlBar A.M. and Hoque M.R., "Enterprise Resource Planning (ERP) Systems: Design, Trends and Deployment", The International Technology Management Review, 5(2), 72 - 81, (2015).
[2] Alsuessi W., "General electricity company of Libya (GECOL)", European International Journal of Science and Technology, 4(1): 61-69, (2015).
[3] Witten I.H., Frank E. and Hall M.A., “Data Mining Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, San Francisco, (2011).
[4] Abusida A.M. and Gultepe Y., "An Association Prediction Model: GECOL as a Case Study", International Journal of Information Technology and Computer Science, 11(10): 34-39, (2019).
[5] Bouckaert R.R., Frank E., Hall M., Kirkby R., Reutemann P., Seewald A. and Scuse D., “WEKA Manual for Version 3-6-10”, Hamilton, University of Waikato, /2013).
[6] Khamaira M.Y., Krzma A. and Alnass A.M., "Long Term Peak Load Forecasting for the Libyan Network", First Conference for Engineering Sciences and Technology (CEST-2018), Libya, (2018).
[7] Slimani T. and Lazzez A., "Efficient Analysis of Pattern and Association Rule Mining Approaches", International Journal of Information Technology and Computer Science, 6(3): 70-81, (2014).
[8] Abusida A.M. and Hancerliogullari A., "A New Approach to Load Shedding Prediction in GECOL Using Deep Learning Neural Network", IJCSNS International Journal of Computer Science and Network Security, 22(3): 220-228, (2022).
[9] Bank W., "Supporting electricity sector reform in Libya: task C - institutional development and performance improvement of GECOL: report 4.2 - improving GECOL technical performance", World Bank Group, Washington, (2017).
[10] Bhojani S. and Bhatt N., "Data Mining Techniques and Trends – A Review", Global Journal for Research Analysis, 5(5): 252-254, (2016).
[11] Bharati M. and Ramageri B.M., "Data mining techniques and applications", Indian Journal of Computer Science and Engineering, 1(4): 301-305 , (2010).
[12] Jain A.K., Murty M.N. and Flynn P.J., "Data Clustering: A Review", ACM Computing Surveys, 31(3): 264-323, (1999).
[13] Zaki M.J. and Meira W., “Data Mining and Machine Learning: Fundamental Concepts and Algorithms”, Cambridge University Press, New York, (2020).
[14] Bishop C.M., “Pattern Recognition and Machine Learning (Information Science and Statistics)”, Springer, India, (2006).
[15] Hastie T., Tibshirani R. and Friedman J.H., “The Elements of Statistical Learning Data Mining: Inference, and Prediction”, Springer,California, (2016).
[16] Duda R.O. and Hart P.E., “Pattern Classification and Scene Analysis”, Wiley, California, (1973).

Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model

Yıl 2023, Cilt: 26 Sayı: 4, 1697 - 1703, 01.12.2023

Ashaf Mohammed Abusıda Seçil Karatay Rezvan Rezaeizadeh Aybaba Hançerlioğulları

https://doi.org/10.2339/politeknik.1368126

Öz

The main goal of this study is to compare the performance of the classification algorithms applied to the SCADA database of the Supervisory Control and Data Acquisition (SCADA) system of the General Electricity Company of Libya (GECOL). The company's annual energy and material losses have become seriously important to the Libyan government's research field. The well-established data mining and classification software package known as the WEKA tool is used to minimize these losses,. As necessary data input for algorithms; six different parameters are taken into consideration, namely power production size, energy production duration, energy production date, ambient temperature, humidity level and wind speed. This study is examined in detail for the first time in this article. In addition, considering the temperature variables, humidity, wind and other atmospheric effects of the environment, the energy losses of the company and the country are reduced to a minimum level. As a result, the company's annual electricity consumption is classified as low, medium or high consumption with the simulations. Thus, in cases where energy consumption is high, it is possible to make accurate and rapid decisions regarding the determination and classification of time periods related to energy consumption.

Anahtar Kelimeler

Gecol, Data mining, Gecol, Scada, Weka

Kaynakça

[1] Bahssas D.M., AlBar A.M. and Hoque M.R., "Enterprise Resource Planning (ERP) Systems: Design, Trends and Deployment", The International Technology Management Review, 5(2), 72 - 81, (2015).
[2] Alsuessi W., "General electricity company of Libya (GECOL)", European International Journal of Science and Technology, 4(1): 61-69, (2015).
[3] Witten I.H., Frank E. and Hall M.A., “Data Mining Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, San Francisco, (2011).
[4] Abusida A.M. and Gultepe Y., "An Association Prediction Model: GECOL as a Case Study", International Journal of Information Technology and Computer Science, 11(10): 34-39, (2019).
[5] Bouckaert R.R., Frank E., Hall M., Kirkby R., Reutemann P., Seewald A. and Scuse D., “WEKA Manual for Version 3-6-10”, Hamilton, University of Waikato, /2013).
[6] Khamaira M.Y., Krzma A. and Alnass A.M., "Long Term Peak Load Forecasting for the Libyan Network", First Conference for Engineering Sciences and Technology (CEST-2018), Libya, (2018).
[7] Slimani T. and Lazzez A., "Efficient Analysis of Pattern and Association Rule Mining Approaches", International Journal of Information Technology and Computer Science, 6(3): 70-81, (2014).
[8] Abusida A.M. and Hancerliogullari A., "A New Approach to Load Shedding Prediction in GECOL Using Deep Learning Neural Network", IJCSNS International Journal of Computer Science and Network Security, 22(3): 220-228, (2022).
[9] Bank W., "Supporting electricity sector reform in Libya: task C - institutional development and performance improvement of GECOL: report 4.2 - improving GECOL technical performance", World Bank Group, Washington, (2017).
[10] Bhojani S. and Bhatt N., "Data Mining Techniques and Trends – A Review", Global Journal for Research Analysis, 5(5): 252-254, (2016).
[11] Bharati M. and Ramageri B.M., "Data mining techniques and applications", Indian Journal of Computer Science and Engineering, 1(4): 301-305 , (2010).
[12] Jain A.K., Murty M.N. and Flynn P.J., "Data Clustering: A Review", ACM Computing Surveys, 31(3): 264-323, (1999).
[13] Zaki M.J. and Meira W., “Data Mining and Machine Learning: Fundamental Concepts and Algorithms”, Cambridge University Press, New York, (2020).
[14] Bishop C.M., “Pattern Recognition and Machine Learning (Information Science and Statistics)”, Springer, India, (2006).
[15] Hastie T., Tibshirani R. and Friedman J.H., “The Elements of Statistical Learning Data Mining: Inference, and Prediction”, Springer,California, (2016).
[16] Duda R.O. and Hart P.E., “Pattern Classification and Scene Analysis”, Wiley, California, (1973).

Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Derin Öğrenme, Bilgisayar Sistem Yazılımı, Yazılım Mühendisliği (Diğer), Elektrik Tesisleri
Bölüm	Araştırma Makalesi
Yazarlar	Ashaf Mohammed Abusıda Seçil Karatay Rezvan Rezaeizadeh Aybaba Hançerlioğulları 0000-0002-9830-4226
Erken Görünüm Tarihi	26 Aralık 2023
Yayımlanma Tarihi	1 Aralık 2023
Gönderilme Tarihi	28 Eylül 2023
Yayımlandığı Sayı	Yıl 2023 Cilt: 26 Sayı: 4

Kaynak Göster

APA	Abusıda, A. M., Karatay, S., Rezaeizadeh, R., Hançerlioğulları, A. (2023). Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model. Politeknik Dergisi, 26(4), 1697-1703. https://doi.org/10.2339/politeknik.1368126
AMA	Abusıda AM, Karatay S, Rezaeizadeh R, Hançerlioğulları A. Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model. Politeknik Dergisi. Aralık 2023;26(4):1697-1703. doi:10.2339/politeknik.1368126
Chicago	Abusıda, Ashaf Mohammed, Seçil Karatay, Rezvan Rezaeizadeh, ve Aybaba Hançerlioğulları. “Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model”. Politeknik Dergisi 26, sy. 4 (Aralık 2023): 1697-1703. https://doi.org/10.2339/politeknik.1368126.
EndNote	Abusıda AM, Karatay S, Rezaeizadeh R, Hançerlioğulları A (01 Aralık 2023) Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model. Politeknik Dergisi 26 4 1697–1703.
IEEE	A. M. Abusıda, S. Karatay, R. Rezaeizadeh, ve A. Hançerlioğulları, “Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model”, Politeknik Dergisi, c. 26, sy. 4, ss. 1697–1703, 2023, doi: 10.2339/politeknik.1368126.
ISNAD	Abusıda, Ashaf Mohammed vd. “Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model”. Politeknik Dergisi 26/4 (Aralık 2023), 1697-1703. https://doi.org/10.2339/politeknik.1368126.
JAMA	Abusıda AM, Karatay S, Rezaeizadeh R, Hançerlioğulları A. Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model. Politeknik Dergisi. 2023;26:1697–1703.
MLA	Abusıda, Ashaf Mohammed vd. “Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model”. Politeknik Dergisi, c. 26, sy. 4, 2023, ss. 1697-03, doi:10.2339/politeknik.1368126.
Vancouver	Abusıda AM, Karatay S, Rezaeizadeh R, Hançerlioğulları A. Improvement of Classification Algorithms for Energy Saving in Lost Energy Data of Libya Electricity Company Using Weka Model. Politeknik Dergisi. 2023;26(4):1697-703.