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

Author:

ABUSIDA Ashaf Mohammed1,KARATAY Seçil1,REZAEİZADEH Rezvan2,HANÇERLİOĞULLARI Aybaba3ORCID

Affiliation:

1. KASTAMONU ÜNİVERSİTESİ

2. GUILAN UNIVERSITY

3. KASTAMONU UNIVERSITY

Abstract

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.

Publisher

Politeknik Dergisi

Reference16 articles.

1. [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. [2] Alsuessi W., "General electricity company of Libya (GECOL)", European International Journal of Science and Technology, 4(1): 61-69, (2015).

3. [3] Witten I.H., Frank E. and Hall M.A., “Data Mining Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, San Francisco, (2011).

4. [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. [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).

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