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Analysis and Prediction of Energy Consumption Using Supervised Machine Learning Techniques: A Study of Libyan Electricity Company Data

  • 투고 : 2023.03.05
  • 발행 : 2023.03.30

초록

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.

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참고문헌

  1. T. Slimani and A. Lazzez, "Efficient Analysis of Pattern and Association Rule Mining Approaches," International Journal of Information Technology and Computer Science, vol. 6, no. 3, pp. 70-81, 2014.  https://doi.org/10.5815/ijitcs.2014.03.09
  2. D. M. Bahssas, A. M. AlBar, and M. R. Hoque, "Enterprise resource planning (ERP) systems: design, trends and deployment," Int. Technol. Manag. Rev., vol. 5, no. 2, pp. 72-81, 2015.  https://doi.org/10.2991/itmr.2015.5.2.2
  3. A. M. Abusida and Y. Gultepe, "An Association Prediction Model: GECOL as a Case Study," unpublished.
  4. W. Alsuessi, "General Electricity Company of Libya (GECOL)," Eur. Int. J. Sci. Technol., vol. 4, no. 1, pp. 1-9, 2015. 
  5. A. M. Abusida and A. Hancerliogullari, "The Power Load Prediction in GECOL using Artificial Neural Network," in press. 
  6. A. M. Abusida and A. Hancerliogullari, "A New Approach to Load Shedding Prediction in GECOL Using Deep Learning Neural Network," IJCSNS International Journal of Computer Science and Network Security, vol. 22, no. 3, pp. 220-228. 
  7. M. Zhou and T. Wang, "Fault diagnosis of power transformer based on association rules gained by rough set," in Proceedings of the 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010, vol. 3, pp. 123-126. 
  8. P.-N. Tan, M. Steinbach, and V. Kumar, "Introduction to Data Mining," 2nd edition, Pearson Education, 2006. 
  9. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," in Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487-499. 
  10. J. Han, M. Kamber, and J. Pei, "Data Mining: Concepts and Techniques," 3rd edition, Morgan Kaufmann, 2011. 
  11. R. R. Bouckaert, E. Frank, M. Hall, R. Kirkby, P. Reutemann, A. Seewald, and D. Scuse, "WEKA Manual," Version 3-6-10, University of Waikato, 2013. 
  12. I. H. Witten, E. Frank, and M. A. Hall, "Data Mining: Practical Machine Learning Tools and Techniques," 2nd edition, Elsevier, 2005. 
  13. K. Mani and R. Akila, "Enhancing the Performance in Generating Association Rules using Singleton Apriori," International Journal of Information Technology and Computer Science, vol. 9, no. 1, pp. 58-64, 2017.  https://doi.org/10.5815/ijitcs.2017.01.07
  14. W. Nisar, A. Khan, and F. Ahmad, "Analysis of Apriori algorithm and improvement of association rule mining," Journal of Emerging Technologies and Innovative Research, vol. 4, no. 3, pp. 100-106, 2017.