| Yazarlar (2) |
Ashraf Mohammed Abusida
|
Prof. Dr. Aybaba HANÇERLİOĞULLARI
Kastamonu Üniversitesi, Türkiye |
| Özet |
| The ever-increasing amount of data generated by various industries and systems has led to the development of data mining techniques as a means to extract valuable insights and knowledge from such data. The electrical energy industry is no exception, with the large amounts of data generated by SCADA systems. This study focuses on the analysis of historical data recorded in the SCADA database of the Libyan Electricity Company. The database, spanned from January 1st, 2013, to December 31st, 2022, contains records of daily date and hour, energy production, temperature, humidity, wind speed, and energy consumption levels. The data was pre-processed and analyzed using the WEKA tool and the Apriori algorithm, a supervised machine learning technique. The aim of the study was to extract association rules that would assist decision-makers in making informed decisions with greater efficiency and reduced costs. The results obtained from the study were evaluated in terms of accuracy and production time, and the conclusion of the study shows that the results are promising and encouraging for future use in the Libyan Electricity Company. The study highlights the importance of data mining and the benefits of utilizing machine learning technology in decision-making processes. |
| Anahtar Kelimeler |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | ESCI dergilerinde yayınlanan tam makale |
| Dergi Adı | IJCSNS International Journal of Computer Science and Network Security |
| Dergi ISSN | 1738-7906 Wos Dergi |
| Dergi Tarandığı Indeksler | Escı |
| Makale Dili | İngilizce |
| Basım Tarihi | 03-2023 |
| Cilt No | 23 |
| Sayı | 3 |
| Sayfalar | 10 / 16 |
| Doi Numarası | 10.22937/IJCSNS.2023.23.3.2 |
| Makale Linki | http://paper.ijcsns.org/07_book/202303/20230302.pdf |
| Atıf Sayıları |
