Home / Regular Issue / JST Vol. 29 (2) Apr. 2021 / JST-1949-2020


Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

Oladimeji Ibrahim, Waheed Olaide Owonikoko, Abubakar Abdulkarim, Abdulrahman Okino Otuoze, Mubarak Akorede Afolayan, Ibrahim Sani Madugu, Mutiu Shola Bakare and Kayode Elijah Adedayo

Pertanika Journal of Social Science and Humanities, Volume 29, Issue 2, April 2021

DOI: https://doi.org/10.47836/pjst.29.2.14

Keywords: Artificial intelligence, fuzzy logic, load forecasting, mean absolute percentage error, MF, short-term

Published on: 30 April 2021

A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

  • Al-Kandari, A. M., Soliman, S. A., & El-Hawary, M. E. (2004). Fuzzy short-term electric load forecasting. International Journal of Electrical Power & Energy Systems, 26(2), 111-122. https://doi.org/10.1016/S0142-0615(03)00069-3

  • Bozkurt, Ö. Ö., Biricik, G., & Tayşi, Z. C. (2017). Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market. PLOS ONE, 12(4), Article e0175915. https://doi.org/10.1371/journal.pone.0175915

  • Černe, G., Dovžan, D., & Škrjanc, I. (2018). Short-term load forecasting by separating daily profiles and using a single fuzzy model across the entire domain. IEEE Transactions on Industrial Electronics, 65(9), 7406-7415. https://doi.org/10.1109/TIE.2018.2795555.

  • Chikobvu, D., & Sigauke, C. (2012). Regression-SARIMA modelling of daily peak electricity demand in South Africa. Journal of Energy in Southern Africa, 23(3), 23-30.

  • Cui, H., & Peng, X. (2015). Short-term city electric load forecasting with considering temperature effects: An improved ARIMAX model. Mathematical Problems in Engineering, 2015, Article 589374. https://doi.org/10.1155/2015/589374

  • Danladi, A. D., Puwu, M. I., Michael, Y., & Garkida, B. M. (2016). Use of Fuzzy Logic To Investigate Weather Parameter Impact on Electrical Load Based on Short Term Forecasting. Nigerian Journal of Technology, 35(3), 562-567. http://dx.doi.org/10.4314/njt.v35i3.14

  • Ding, Y., Neumann, M. A., Da Silva, P. G., & Beigl, M. (2013). A framework for short-term activity-aware load forecasting. In Joint Proceedings of the Workshop on AI Problems and Approaches for Intelligent Environments and Workshop on Semantic Cities (pp. 23-28). Association for Computing Machinery. https://doi.org/10.1145/2516911.2516919

  • Dudek, G. (2016). Pattern-based local linear regression models for short-term load forecasting. Electric Power Systems Research, 130, 139-147. https://doi.org/10.1016/j.epsr.2015.09.001

  • Emarati, M., Keynia, F., & Askarzadeh, A. (2019). Application of hybrid neural networks combined with comprehensive learning particle swarm optimization to short-term load forecasting. Computational Intelligence in Electrical Engineering, 10(1), 33-40. http://dx.doi.org/10.22108/isee.2017.21744

  • Fan, G. F., Peng, L. L., Hong, W. C., & Sun, F. (2016). Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing, 173, 958-970. https://doi.org/10.1016/j.neucom.2015.08.051

  • Faysal, M., Islam, M. J., Murad, M. M., Islam, M. I., & Amin, M. R. (2019). Electrical load forecasting using fuzzy system. Journal of Computer and Communications, 7(9), 27-37. http://doi.org/10.4236/jcc.2019.79003

  • Ganguly, A., Goswami, K., Mukherjee, A., & Sil, A. K. (2019). Short-term load forecasting for peak load reduction using artificial neural network technique. In U. Biswas, A. Banerjee, S. Pal, A. Biswas, D. Sarkar & S. Haldar (Eds.), Advances in computer, communication and control (pp. 551-559). Springer. https://doi.org/10.1007/978-981-13-3122-0_56

  • Ganguly, P., Kalam, A., & Zayegh, A. (2017, May 18-21). Short term load forecasting using fuzzy logic. In International Conference on Research in Education and Science (pp. 355-361). Ephesus, Turkey.

  • Gohil, P., & Gupta, M. (2014). Short term load forecasting using fuzzy logic 1. Journal of Engineering Development and Research, 10(3), 127-130

  • Jetcheva, J. G., Majidpour, M., & Chen, W. P. (2014). Neural network model ensembles for building-level electricity load forecasts. Energy and Buildings, 84, 214-223. https://doi.org/10.1016/j.enbuild.2014.08.004

  • Kumar, S., Mishra, S., & Gupta, S. (2016, February). Short term load forecasting using ANN and multiple linear regression. In 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT) (pp. 184-186). Conference Publishing Services. https://doi.org/10.1109/CICT.2016.44

  • Kuster, C., Rezgui, Y., & Mourshed, M. (2017). Electrical load forecasting models: A critical systematic review. Sustainable Cities and Society, 35, 257-270. https://doi.org/10.1016/j.scs.2017.08.009

  • Lei, J., Jin, T., Hao, J., & Li, F. (2019). Short-term load forecasting with clustering–regression model in distributed cluster. Cluster Computing, 22(4), 10163-10173. https://doi.org/10.1007/s10586-017-1198-4

  • Mi, J., Fan, L., Duan, X., & Qiu, Y. (2018). Short-term power load forecasting method based on improved exponential smoothing grey model. Mathematical Problems in Engineering, 2018, Article 3894723. https://doi.org/10.1155/2018/3894723

  • Peng, Y., Wang, Y., Lu, X., Li, H., Shi, D., Wang, Z., & Li, J. (2019, May). Short-term load forecasting at different aggregation levels with predictability analysis. In 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia) (pp. 3385-3390). Institute of Electrical and Electronics Engineers, Inc. http://doi.org/10.1109/ISGT-Asia.2019.8881343

  • Razak, I. A. W. A., Majid, S., Aras, M. S. M., & Ahmad, A. (2012). Electricity load forecasting using data mining technique. In A. Karahoca (Ed.), Advance in data mining knowledge discovery and application (pp. 235-254). IntechOpen. http://dx.doi.org/10.5772/48657

  • Rizwan, M., Kumar, D., & Kumar, R. (2012). Fuzzy logic approach for short term electrical load forecasting. Electrical and Power Engineering Frontier, 1(1), 8-12.

  • Sadaei, H. J., e Silva, P. C. L., Guimarães, F. G., & Lee, M. H. (2019). Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series. Energy, 175, 365-377. https://doi.org/10.1016/j.energy.2019.03.081

  • Silva, G. C., Silva, J. L., Lisboa, A. C., Vieira, D. A., & Saldanha, R. R. (2017, November). Advanced fuzzy time series applied to short term load forecasting. In 2017 IEEE Latin American Conference on Computational Intelligence (LA-CCI) (pp. 1-6). IEEE Computational Intelligence Society. http://doi.org/10.1109/LA-CCI.2017.8285726

  • Singhal, R., Choudhary, N. K., & Singh, N. (2020). Short-term load forecasting using hybrid ARIMA and artificial neural network model. In D. Dutta, H. Kar, C. Kumar & V. Bhadauria (Eds.), Advances in VLSI, Communication, and Signal Processing (pp. 935-947). Springer. https://doi.org/10.1007/978-981-32-9775-3_83

  • Siri, S. T. (2018). Short-term load forecasting using a hybrid of genetic algorithm (Ga) and particle swarm optimization (Pso) for an optimized neural network [Doctoral dissertation, University of Nairobi]. University of Nairobi Publications. http://hdl.handle.net/11295/105048

  • Sivanandam, S. N., Sumathi, S., & Deepa, S. N. (2007). Introduction to fuzzy logic using MATLAB (Vol. 1). Springer.

  • Srivastava, A. K., Pandey, A. S., & Singh, D. (2016, March). Short-term load forecasting methods: A review. In 2016 International conference on emerging trends in electrical electronics & sustainable energy systems (ICETEESES) (pp. 130-138). Institute of Electrical and Electronics Engineering, Inc. http://doi.org/10.1109/ICETEESES.2016.7581373

  • WeatherOnline, W. (2019). Hourly temperature data of Ilorin, Nigeria. Retrieved April 24, 2019, from www.worldweatheronline.com.

  • Wen, Z., Xie, L., Fan, Q., & Feng, H. (2020). Long term electric load forecasting based on TS-type recurrent fuzzy neural network model. Electric Power Systems Research, 179, Article 106106. https://doi.org/10.1016/j.epsr.2019.106106

  • Yu, F., & Xu, X. (2014). A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Applied Energy, 134, 102-113. https://doi.org/10.1016/j.apenergy.2014.07.104

ISSN 0128-7702

e-ISSN 2231-8534

Article ID


Download Full Article PDF

Share this article

Related Articles