Article Per Year (5 Year)
p-Index From 2020 - 2025
0.444
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Journal of Robotics and Control (JRC)
Tran, Thanh Ngoc
Unknown Affiliation
Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Research on the impact of sliding window and differencing procedures on the support vector regression model for load forecasting Tran, Thanh Ngoc; Dang, Thi Phuc; Lam, Binh Minh; Nguyen, Anh Tuan
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1314-1322

Abstract

Load forecasting is a critical aspect of energy management and grid operations. Machine learning techniques as support vector regression (SVR), have been widely used for load forecasting. However, the effectiveness of SVR is highly dependent on its hyperparameters, including the error sensitivity parameter, penalty factor, and kernel function. Furthermore, as the load data follows a time series pattern, the accuracy of the SVR model is influenced by the data's characteristics. In this regard, the present study aims to investigate the impact of combining the sliding window procedure and differencing the input data on the prediction accuracy of the SVR model. The study utilizes daily maximum load data from the Queensland and Victoria states in Australia. The analyses revealed that while the sliding window procedure had a minimal impact on the prediction results, the differencing of the input data significantly improved the accuracy of the predictions.
A Hybrid Transformer-MLP Approach for Short-Term Electric Load Forecasting Nguyen, Tuan Anh; Tran, Thanh Ngoc
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26960

Abstract

Short-term electric load forecasting plays a vital role in ensuring the stability and efficiency of smart grid operations. However, accurately predicting demand remains challenging due to nonlinearity, volatility, and long-term temporal dependencies in consumption patterns. The research proposes a lightweight hybrid deep learning model that integrates a Transformer encoder with a multi-layer perceptron (MLP) to enhance prediction accuracy and robustness for short-term load forecasting. The proposed model employs a Transformer to extract long-range temporal features through self-attention mechanisms, while the MLP captures complex nonlinear mappings at the output stage. A real-world electricity load dataset collected from three Australian states (NSW, QLD, VIC) between 2009 and 2014 is used for evaluation. To assess model performance, mean absolute percentage error (MAPE), mean squared error (MSE), and Root Mean Squared Error (RMSE) are used. Experimental results demonstrate that the proposed transformer-MLP model consistently achieves the lowest forecasting error across all regions. MAPE ranges from 0.69% to 0.95%, outperforming standard deep learning models, including LSTM, CNN, and MLP. Despite its shallow architecture and reduced computational complexity, the hybrid model effectively captures both temporal dependencies and nonlinear variations. This study provides a practical, deployable forecasting solution for smart grids. Future work will extend the model to multi-step forecasting, incorporate exogenous variables such as weather and calendar effects, and explore deeper Transformer variants further to enhance prediction accuracy and generalization across diverse load conditions.
Co-Authors Dang, Thi Phuc Lam, Binh Minh Nguyen, Anh Tuan Nguyen, Tuan Anh