Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Mohammed A. T. Alrubei
Al-Furat Al-Awsat Technical University
Control & Systems Engineering Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
TCM-Former: a transformer with temporal convolution for photovoltaic power forecasting Sarab Al-Chlaihawi; Mohammed A. T. Alrubei; Faris A. Alhaddad
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i3.pp2127-2148

Abstract

To tackle the problem of modeling long-term trends and short-term and high-frequency variations in PV time series, a strong and efficient forecasting model of photovoltaic (PV) power generation is advanced. This paper presents STL-TCM-Former, a hybrid model that breaks down the raw PV signal with seasonal-trend decomposition with LOESS (STL) into trend and seasonal components. These elements are after that processed via a dual path encoder decoder transformer architecture that is improved with a temporal convolutional module (TCM). The period (transformer-based) path is used for capturing the global, long-range dependencies in seasonal component and temporal (TCM) path is used to extract the localized, short-term dynamics. Trend component is processed with a dedicated TCM branch, minimizing component interferences and providing a multiscale temporal representation, specific to PV generation patterns. The proposed model was evaluated on the Yulara (Uluru) solar dataset under short-term (60-hour) and long-term (300-hour) forecasting horizons. Compared with benchmark models including LSTM, WOA-LSTM, VMD-LSTM, WOA-VMD-LSTM, and hybrid WOA/VMD/LSTM configurations, STL-TCM-former achieved superior performance with R² = 99.76%, MAPE ≈ 2.24%, RMSE = 16.63, and MAE = 10.36. In addition, the PJM interconnection dataset was also employed to evaluate the generalization capability of the proposed method. The results demonstrate high accuracy, stability, and strong generalization capability under varying environmental conditions.
Co-Authors Faris A. Alhaddad Sarab Al-Chlaihawi