Determination of Electricity Production by Fuzzy Logic Method

Abstract

With the increase in the need for electrical energy, production amount planning is of great importance in order not to experience restrictions in terms of use, to meet the required electricity production, and to evaluate the excess production efficiently. In this study, a generation forecasting model was created with the fuzzy logic method to determine the electricity generation strategy. The created model is aimed to determine the electrical energy that needs to be produced daily by using the previous day's production amount, temperature, and season data. Three separate sets of data were used to test the fuzzy logic model built using information from the General Directorate of Meteorology (GDM) and Energy Markets Operations Inc. (EMOI). Fuzzy Logic was used to predict the data and the accuracy rates were found to be high. An improvement was observed when the accuracy rates were compared with the accuracy rates obtained in the Multiple Linear Regression Model. The accuracy rates of the model were initially examined using the Fuzzy Logic approach on weekdays and weekends, followed by a seasonal analysis and an assessment of the model's performance. As a result of the analysis, it was observed that the model worked with high accuracy in the autumn season and on weekend days.

Keywords

• Energy
• Electrical Energy
• Fuzzy Logic

References

1. Gök, K., Electrical energy consumption, Turkey evaluation and analysis with analytical hierarchy process, Master Thesis, Istanbul Technical University Energy Institute, 172, 2016.
2. Akgül, H. and Gözen, M., An evaluation of the factors affecting the choice of resources in electricity production, Journal of Humanities and Tourism Research, 10(4) : 919-938,2020.
3. Energy Markets Supervisory Authority, Electricity Annual Sector Report, Date of Access: 2022 Available: https://www.epdk.gov.tr/Detay/Icerik/3-0-24-3/elektrikyillik-sektor-raporu
4. Najmi, M. and Dalimi, R., Electrical energy needs projection of Bangka Belitung province in 2019-2033 using fuzzy logic, International Conference on Electrical Engineering and Computer Science, Bandung, 176-180, 2019.
5. Kazemzadeh, M. R., Amjadian, A. and Turaj, A., A hybrid data mining driven algorithm for long term electric peak load and energy demand forecasting, Energy, 204: 117948, 2020.
6. Olivera, E. M. and Olivera, F. L. C., Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods, Energy, 144: 776-788, 2018.
7. Yükseltan, E., Yücekaya, A. and Bilge A. H., Forecasting electricity demand for Turkey: Modeling periodic variations and demand segregation, Applied Energy, 193 : 287-296, 2017.
8. Çevik, H.H. and Çunkaş, M., Short-term load forecasting using fuzzy logic and ANFIS, Neural Computing Applications, 26 : 1355-1367, 2015.

9. Williams, S. and Short, M., Electricity demand forecasting for decentralised energy management, Energy and abauilt Environment, 1: 178-186, 2020.
10. Açıkgöz, N., Improving EFQM excellence model measurement performance with fuzzy logic approach, M.Sc., Sakarya University, Institute of Science and Technology, 94 (2019).
11. Tutorial Points, AI Fuzzy Logic Systems, Access: 2022, Available:_https://www.tutorialspoint.com/artificial_intelligence/artificial_intelligence_fuzzy_logic_systems.htm
12. EMOI, Transparency Platform-Real-Time Production Data, Date of Access: 2022 Available:https://seffaflik.epias.com.tr/transparency/uretim/gerceklesen-uretim/gercek-zamanli-uretim.xhtml
13. GDM, Temperature Analysis, Date of Access: 2022 Available:https://www.mgm.gov.tr/veridegerlendirme/sicaklik-analizi.aspx
14. Kişi, Ö., Applicability of Mamdani and Sugeno fuzzy genetic approaches for modeling reference evapotranspiration, Journal of Hydrology, 504 : 160-170, 2013.
15. Li, K., Ma, Z., Robinson, D., Lin, W. and Li, Z., A data-driven strategy to forecast next-day electricity usage and peak electricity demand of a building portfolio using cluster analysis, Cubist regression models and Particle Swarm Optimization, Journal of Cleaner Production, 273:123115, 2020.
16. Runge, J., Zmeureanu, R. and Cam, M., Hybrid short-term forecasting of the electric demand of supply fans usinh machine learning, Journal of Building Engineering, 20:101144, 2020.
17. Zadeh, L. A., Fuzzy sets, Information and Control, 8 : 338-353 (1965).
18. Zadeh, L. A., Fuzzy algorithms”, Information and Control, 12: 94-102 (1968).
19. Çetin, E., The application of fuzzy logic in production planning and its comparison with the current situation, Master Thesis, Gazi University Institute of Science and Technology, 117, 2019.
20. Janarthanan, R., Balamurali, R., Annapoorani, A. and Vimala, V., Prediction of rainfall using fuzzy logic, Meterials Today: Proceedings, 37: 959-963, 2021.
21. Incekara, C. Ö., Turkey and EU's energy strategies and policies, Journal of Turkish Operations Management, 3 : 298-313, 2019.
22. Özkan, E., Güler, E. and Aladağ, Z., Choosing an appropriate estimation method for electrical energy consumption data, Industrial Engineering, 31(2) :198-214, 2020.
23. Rezk, H., Inayat, A., Abdelkareem, M. A., Olabi, A. G. and Nassef, A. M., Optimal operating parameter determination based on fuzzy logic modeling and marine predators algorithm approaches to improve the methane production via biomass gasification, Energy, 239 : 122072, 2022.
24. Özdemir, O. and Kalınkara, Y., Fuzzy logic: A content analysis of thesis and article studies between 2000-2020, ACTA Infologica, 4(2) : 155-174, 2020.
25. Ross, T. J., Fuzzy Logic With Engineering Applications, 3rd ed., A John Wiley and Soons, New York, 602, 2010.
26. Pirbazari, A., Farmanbar, M., Chakravorty, A. and Rong, C., Short-term load forecasting using smart meter data: a generalization analysis, Processes, 8 : 484-505, 2020.
27. Busisiwe, R. L., Mbuyu, S. and Reginald, N. N., A fuzzy logic based residential electrical energy optimization system based on time of use tarrifs”, International Energy Journal, 21 : 415-426, 2021.
28. Çelik, A. N. and Özgür, E., Review of Turkey’s photovoltaic energy status: Legal structure, existing installed power and comparative analysis, Renewable and Sustainable Energy Reviews, 134 : 1120344, 2020.
29. Gosmann, L., Geitner, C., Wieler, N., Data-driven forward osmosis model development using multiple linear regression and artificial neural networks, Computer and Chemical Engineering, 165 : 107933, 2022.
30. Şahin, H. and Esen, H., The usage of renewable energy sources and its effects on GHG emission intensity of electricity generation in Turkey, Renewable Energy, 192 : 859 – 869, 2022.
31. Uğur, D., Mamdani Type Fuzzy Logic Based Greenhouse Climate Control System Design, Master Thesis, Karamanoğlu Mehmetbey University Institute of Science, 63, 2021.
32. Karadağ Albayrak, Ö., The forecasting of renewable energy generation for turkey by arti¦cial neural networks and a autoregressive ıntegrated movingaverage model -2023 generation targets by renewable energy resources, Efficiency Magazine, 2023, Cilt:57, Sayı:1, pp.121-138
33. L. M. Olaru, A. Gellert, U. Fiore and F. Palmieri, Electricity production and consumption modeling through fuzzy logic, International Journal of Intelligent Systems published by Wiley Periodicals LLC, Int J Intell Syst. 2022, pp.8348–8364.