A Hybrid Fuzzy-Kalman Filtering Approach for Short-Term Electricity Load Forecasting in the Kurdistan Region of Iraq

Rebaz O. Yahya; Kurdistan I. Mawlood

Article Details

A Hybrid Fuzzy-Kalman Filtering Approach for Short-Term Electricity Load Forecasting in the Kurdistan Region of Iraq

Authors:

Rebaz O. Yahya | Kurdistan I. Mawlood

Publisher:

Cihan University, Iraq

Journal:

Cihan University-Erbil Scientific Journal Vol.9 Issue 2, pp.117-124

Document Type:

Article

Publication Date:

12/2025

Document Language:

English

Document ID (Org No.):

2001837363

pISSN:

2707-6377

Keywords:

Abstract:

 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.

Article Details

A Hybrid Fuzzy-Kalman Filtering Approach for Short-Term Electricity Load Forecasting in the Kurdistan Region of Iraq

Authors:

Rebaz O. Yahya | Kurdistan I. Mawlood

Publisher:

Cihan University, Iraq

Journal:

Cihan University-Erbil Scientific Journal Vol.9 Issue 2, pp.117-124

Document Type:

Article

Publication Date:

12/2025

Document Language:

English

Document ID (Org No.):

2001837363

pISSN:

2707-6377

Short-Term Load Forecasting (STLF)

Fuzzy Logic Model

Kalman Filter

Hybrid Modelling

Electricity Demand

Issues

Article Details

A Hybrid Fuzzy-Kalman Filtering Approach for Short-Term Electricity Load Forecasting in the Kurdistan Region of Iraq

Authors: Rebaz O. Yahya | Kurdistan I. Mawlood

Publisher: Cihan University, Iraq

Journal: Cihan University-Erbil Scientific Journal Vol.9 Issue 2, pp.117-124

Document Type: Article

Publication Date: 12/2025

Document Language: English

Document ID (Org No.): 2001837363

pISSN: 2707-6377

Short-Term Load Forecasting (STLF)

Fuzzy Logic Model

Kalman Filter

Hybrid Modelling

Electricity Demand

Issues

Authors:

Rebaz O. Yahya | Kurdistan I. Mawlood

Publisher:

Cihan University, Iraq

Journal:

Cihan University-Erbil Scientific Journal Vol.9 Issue 2, pp.117-124

Document Type:

Article

Publication Date:

12/2025

Document Language:

English

Document ID (Org No.):

2001837363

pISSN:

2707-6377