A Hybrid Fuzzy-Kalman Filtering Approach for Short-Term Electricity Load Forecasting in the Kurdistan Region of Iraq
DOI:
https://doi.org/10.24086/cuesj.v9n2y2025.pp117-124Keywords:
Short-Term Load Forecasting (STLF), Fuzzy Logic Model, Kalman Filter, Hybrid Modelling, Electricity DemandAbstract
Accurate short-term load forecasting is vital for maintaining the reliability and stability of electricity supply systems. This study examines forecasting methods for the Kurdistan region of Iraq, where electricity demand is steadily rising. Historical hourly load data and relevant exogenous variables were used to develop and compare two models: A standalone Fuzzy Logic model and a Hybrid Fuzzy-Kalman Filter (KF) model. Although both Fuzzy Logic and KFs have been used separately for load forecasting, there has been a lack of literature directly comparing a standalone Fuzzy Logic model with a Hybrid Fuzzy-KF approach, especially using data from the Kurdistan region. The Fuzzy Logic model was designed to capture non-linear relationships and apply human-like reasoning in predicting load demand. The hybrid approach integrated a KF to refine initial fuzzy estimates by reducing noise and updating predictions based on recent observations. Both models were evaluated using mean absolute percentage error and root mean square error (RMSE). The outcomes show that standalone fuzzy logic prediction is not as effective as the Hybrid Fuzzy–KF. The hybrid technique lowers the average mean absolute error and RMSE of the fuzzy model to 95.13 MW (−3.75%) and 109.89 MW (−10.46%), respectively, compared to the fuzzy model’s average of 98.84 MW and 122.73 MW. This enhancement demonstrates how well noise filtering and recursive state estimation work, especially when load circumstances are steady. Results show that the hybrid model consistently outperformed the standalone Fuzzy Logic model. The findings highlight that the Hybrid Fuzzy-KF provides a more effective solution for short-term load forecasting in Kurdistan’s electricity sector.
Downloads
References
V. Kumar and R. K. Mandal. Long-term load forecasting for smart grid. Engineering Research Express, vol. 6, no. 4, p. 045339, 2024. DOI: https://doi.org/10.1088/2631-8695/ad8f92
2. M. Ali, M. Adnan, M. Tariq and H. V. Poor. Load forecasting through estimated parametrized-based fuzzy inference system in smart grids. IEEE Transactions on Fuzzy Systems, vol. 29, no. 1, pp. 156-165, 2020. DOI: https://doi.org/10.1109/TFUZZ.2020.2986982
3. M. Chavan. Time series analysis in electrical load forecasting. Panamerican Mathematical Journal, vol. 35, no. 1, pp. 210-219, 2024. DOI: https://doi.org/10.52783/pmj.v35.i1s.2307
4. X. Sun, X. Zhang and F. Zeng. An Improved Time Series Temperature Prediction Algorithm Based on Kalman Filter and LSTM. In: 2024 International Conference on Energy and Electrical Engineering (EEE), pp. 1-7, 2024. DOI: https://doi.org/10.1109/EEE59956.2024.10709749
5. S. H. Murad, N. B. Tayfor, N. H. Mahmood and L. Arman. Hybrid genetic algorithms-driven optimization of machine learning models for heart disease predictio. MethodsX, vol. 15, p. 103510, 2025. DOI: https://doi.org/10.1016/j.mex.2025.103510
6. I. Akhtar, S. Kirmani, M. Ahmad and S. Ahmad. Average monthly wind power forecasting using fuzzy approach. IEEE Access, vol. 9, pp. 30426-30440, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3056562
7. H. A. Khidir, İ. Etikan, D. H. Kadir, N. H. Mahmood and R. Sabetvand. Bayesian machine learning analysis with Markov Chain Monte Carlo techniques for assessing characteristics and risk factors of Covid-19 in Erbil City-Iraq 2020-2021. Alexandria Engineering Journal, vol. 78, pp. 162-174, 2023. DOI: https://doi.org/10.1016/j.aej.2023.07.052
8. N. H. Mahmood and D. H. Kadir. An elastic net approach to logistic regression for genetic selection in high-dimensional brain cancer data. Cihan University-Erbil Scientific Journal, vol. 9, no. 1, pp. 14-23, 2025. DOI: https://doi.org/10.24086/cuesj.v9n1y2025.pp14-23
9. A. Ahmad, N. Javaid, A. Mateen, M. Awais and Z. A. Khan. Shortterm load forecasting in smart grids: An intelligent modular approach. Energies, vol. 12, no. 1, p. 164, 2019. DOI: https://doi.org/10.3390/en12010164
10. G. Revach, N. Shlezinger, Sloun, V and Y. C. Eldar. Kalmannet: Data-Driven Kalman Filtering. Institute of Electrical and Electronics Engineers, Piscataway, 2021. DOI: https://doi.org/10.1109/ICASSP39728.2021.9413750
11. T. Wen, J. Liu, B. Cai and C. Roberts. High-precision state estimator design for the state of gaussian linear systems based on deep neural network Kalman Filter. IEEE Sensors Journal, vol. 23, no. 24, pp. 31337-31344, 2023. DOI: https://doi.org/10.1109/JSEN.2023.3329491
12. S. Shen, J. Chen, G. Yu, Z. Zhai and P. Han. KalmanFormer: Using transformer to model the Kalman Gain in Kalman Filters. Frontiers in Neurorobotics, vol. 18, p. 1460255, 2025. DOI: https://doi.org/10.3389/fnbot.2024.1460255
13. W. Liu, Y. Liu and R. Bucknall. A robust localization method for unmanned surface vehicle (USV) navigation using fuzzy adaptive Kalman filtering. IEEE Access, vol. 7, pp. 46071-46083, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2909151
14. I. Ullah, M. Fayaz and D. Kim. Improving accuracy of the Kalman filter algorithm in dynamic conditions using ANN-based learning module. Symmetry, vol. 11, no. 1, p. 94, 2019. DOI: https://doi.org/10.3390/sym11010094
15. M. A. Hammad, B. Jereb, B. Rosi and D. Dragan. Methods and models for electric load forecasting: A comprehensive review. Logistics and Sustainable Transport, vol. 11, no. 1, pp. 51-76, 2020. DOI: https://doi.org/10.2478/jlst-2020-0004
16. U. Dilawar, A. Khaliq and N. Kureshi. Evaluating artificial intelligence and statistical methods for electric load forecasting. International Journal of Innovations in Science and Technology, vol. 3, no. 5, pp. 59-83, 2022. DOI: https://doi.org/10.33411/IJIST/2022030505
17. O. M. Al-Zawbaee and N. H. Mahmood. Building an efficient model by using panel data. Cihan University-Erbil Journal of Humanities and Social Sciences, vol. 7, no. 1, pp. 82-87, 2023. DOI: https://doi.org/10.24086/cuejhss.v7n1y2023.pp82-87
18. N. Hussen Mahmood, D. Hussen Kadir and O. M. M. Alzawbaee. Building a statistical model to forecast traffic accidents for death and injuries by using bivariate time series analysis. Zanco Journal of Humanity Sciences, vol. 28, no. 1, pp. 278-289, 2024. DOI: https://doi.org/10.21271/zjhs.28.18
19. K. W. Yu, C. H. Hsu and S. M. Yang. A Model Integrating ARIMA and ANN with Seasonal and Periodic Characteristics for Forecasting Electricity Load Dynamics in a State. In: Proceedings of the 2019 IEEE 6th International Conference on Energy Smart Systems, 2019.
20. A. A. Kindo, G. Kaladzavi, S. Malo, G. Camara, T. Marie and N. Kolyang. Fuzzy Logic Approach for Knowledge Modeling in an Ontology: A Review. In: IEEE 2nd International Conference on Smart, pp. 1-8, 2020. DOI: https://doi.org/10.1109/SCCIC51516.2020.9377335
21. B. K. Chauhan and P. K. Shukla. Electric Load Forecasting using Fuzzy Knowledge Base System with Improved Accuracy. Intelligent Learning for Computer Vision. Springer Nature, Germany, pp. 259-273, 2021. DOI: https://doi.org/10.1007/978-981-33-4582-9_21
22. D. V. N. Ananth, L. V. S. Kumar, T. S. Gorripotu and A. T. Azar. Design of a fuzzy logic controller for short-term load forecasting with randomly varying load. International Journal of Sociotechnology and Knowledge Development, vol. 13, no. 4, pp. 32-49, 2021. DOI: https://doi.org/10.4018/IJSKD.2021100103
23. A. T. Nguyen, T. Taniguchi, L. Eciolaza, V. Campos, R. Palhares and M. Sugeno. Fuzzy control systems: Past, present and future. IEEE Computational Intelligence Magazine, vol. 14, no. 1, pp. 56-68, 2019. DOI: https://doi.org/10.1109/MCI.2018.2881644
24. Y. Bai, B. Yan, C. Zhou, T. Su and X. Jin. State of art on stateestimation: Kalman filter driven by machine learning. Annual Reviews in Control, vol. 56, pp. 100909-100909, 2023. DOI: https://doi.org/10.1016/j.arcontrol.2023.100909
25. H. H. Afshari, S. A. Gadsden and S. Habibi. Gaussian filters for parameter and state estimation: A general review of theory and recent trends. Signal Processing, vol. 135, pp. 218-238, 2017. DOI: https://doi.org/10.1016/j.sigpro.2017.01.001
26. P. S. Madhukar and L. B. Prasad. State Estimation using Extended Kalman Filter and Unscented Kalman Filter. In: 2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3), pp. 1-4, 2020. DOI: https://doi.org/10.1109/ICONC345789.2020.9117536
27. Z. Li, W. Yang and D. Ding. Time-varying noise statistic estimator based adaptive simplex cubature Kalman filter. Mathematical Problems in Engineering, vol. 2017, no. 1, pp. 1-8, 2017. DOI: https://doi.org/10.1155/2017/5349879
28. S. Akhlaghi, N. Zhou and Z. Huang. Adaptive Adjustment of Noise Covariance in Kalman Filter for Dynamic State Estimation. In: Power and Energy Society General Meeting, 2017. DOI: https://doi.org/10.1109/PESGM.2017.8273755
29. H. Bai and C. N. Taylor. A Variational Bayesian Approach for Estimating System Parameters and Process Noise. In: IEEE 23rd International Conference on Information Fusion, pp. 1-7, 2020. DOI: https://doi.org/10.23919/FUSION45008.2020.9190234
30. P. B. Sreeharsha and V. R. Devanaboyina. Fuzzy Logic-based Adaptive Extended Kalman Filter Algorithm for GNSS Receivers. Defence Science Journal, vol. 68, no. 6, p. 560, 2018. DOI: https://doi.org/10.14429/dsj.68.12313
31. N. Nireekshana, R. Ramachandran and G. V. Narayana. A new soft computing fuzzy logic frequency regulation scheme for two area hybrid power systems. International Journal of Electrical and Electronics Research, vol. 11, no. 3, pp. 705-710, 2023. DOI: https://doi.org/10.37391/ijeer.110310
32. S. Mokhtari and K. K. Yen. A Novel Bilateral Fuzzy Adaptive Unscented Kalman Filter and its Implementation to Nonlinear Systems with Additive Noise. In: IEEE Industry Applications Society Annual Meeting, pp. 1-6, 2020. DOI: https://doi.org/10.1109/IAS44978.2020.9334835
33. G. Dewantoro. Optimizing Membership Function of Fuzzy Logic Controller Via Orthogonal Array: A Case Study on Temperature Control. In: 7th IEEE International Conference on Control System, Computing and Engineering, pp. 192-197, 2017. DOI: https://doi.org/10.1109/ICCSCE.2017.8284403
34. O. Ibrahim, W. O. Owonikoko1, A. Abdulkarim, A. O. Otuoze1, M. A. Afolayan, I. S. Madugu, M. S. Bakare and K. E. Adedayo. Performance evaluation of different membership function in fuzzy logic based short-term load forecasting. Pertanika Journal of Science and Technology, vol. 29, no. 2, pp. 959-978, 2021. DOI: https://doi.org/10.47836/pjst.29.2.14
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Rebaz O. Yahya, Kurdistan I. Mawlood
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-ND 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
