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All Journal Bulletin of Electrical Engineering and Informatics Buletin Ilmiah Sarjana Teknik Elektro Journal of Robotics and Control (JRC) Bulletin of Chemical Reaction Engineering & Catalysis
Nguyen, Tuan Anh
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Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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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.
Enhanced Photocatalytic Performance of Ag-Modified ZnO for the Degradation of Tartrazine Dye Thi, Cam Vi Dao; Nguyen, Tuan Anh; Pham, Quang Minh; Vu, Anh-Tuan
Bulletin of Chemical Reaction Engineering & Catalysis 2025: BCREC Volume 20 Issue 3 Year 2025 (October 2025)
Publisher : Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/bcrec.20443

Abstract

In this study, ZnO materials were synthesized using the hydrothermal method, and then modified with Ag using glucose, a biologically derived and environmentally friendly reducing agent, to produce Ag/ZnO materials with varying Ag contents. The obtained material samples were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and photoluminescence spectroscopy (PL) to determine the crystal structure, surface morphology, and optical properties, respectively. The results showed that the Ag/ZnO sample containing 5 % Ag (Ag/ZnO-5 %) was able to completely decompose Tartrazine (TA) dye after 80 min of irradiation with an 85 W UV lamp, with a first-order reaction rate constant k = 0.03789 min-1 and degradation capacity of 20 mg/g. In comparison, pure ZnO achieved an efficiency of less than 60 %. Factors affecting the photodegradation efficiency, such as initial TA concentration, catalyst dosage, and pH of the solution, were investigated to optimize the reaction conditions. In addition, the Ag/ZnO material exhibited high degradation efficiency toward various organic pollutants, such as Janus Green B (JGB), Congo red (C-Red), Methylene blue (MB), and Caffeine, indicating its potential for broad applications in wastewater treatment. Notably, the investigation of different irradiation light sources (UV, visible light, and sunlight) revealed that sunlight could promote complete degradation of TA in only 20 min of exposure. The photocatalytic reaction mechanism was also proposed to clarify the role of Ag as well as ZnO in enhancing the performance of the Ag/ZnO material system. Copyright © 2025 by Authors, Published by BCREC Publishing Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).
Analysis of Swarm Size and Iteration Count in Particle Swarm Optimization for Convolutional Neural Network Hyperparameter Optimization in Short-Term Load Forecasting Nguyen, Tuan Anh; Nguyen, Trung Dung
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13953

Abstract

Short-term load forecasting (STLF) is critical in modern power system planning and operation. However, the effectiveness of deep learning models such as Convolutional Neural Networks (CNNs) depends on selecting hyperparameters, which are traditionally tuned through time-consuming trial-and-error processes. The research contribution of this study is to systematically analyze how two key parameters—swarm size and iteration count—in Particle Swarm Optimization (PSO) affect the performance of CNN hyperparameter tuning for STLF. A CNN architecture with fixed convolutional depth is optimized using PSO over selected hyperparameters, including the number of filters, batch size, and training epochs. The experiments use two regional Australian electricity load datasets: New South Wales (NSW) and Queensland (QLD). A three-fold cross-validation strategy is employed, and the Mean Absolute Percentage Error (MAPE) is used as the primary evaluation metric. The results show that optimal PSO configurations vary significantly between datasets, with smaller swarm sizes and moderate iteration counts yielding favorable trade-offs between forecasting accuracy and computational cost. However, the reliance on MAPE, sensitivity to near-zero values, and fixed CNN architecture impose limitations. This study provides practical guidance for selecting PSO settings in deep learning-based STLF and demonstrates that tuning PSO configurations can significantly enhance model performance while reducing computational overhead. Future work may explore adaptive or hybrid optimization methods and extend to more diverse forecasting scenarios.
Two-stage random search–Bayesian optimization for CNN-based short-term load forecasting Nguyen, Tuan Anh; Ngoc Tran, Thanh
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11030

Abstract

This study proposes a two-stage hyperparameter optimization pipeline for convolutional neural network (CNN)–based short-term electricity load forecasting. In the first stage, random search is used to broadly explore candidate configurations, including the number of filters in each convolutional layer, batch size, training epochs, and the loss function. In the second stage, Bayesian optimization based on the tree-structured Parzen estimator (TPE), implemented in Optuna, refines promising regions of the hyperparameter space to obtain a better-performing model. The optimized CNN is evaluated using half-hourly (30-minute) electricity demand data from New South Wales (NSW), Victoria (VIC), and Queensland (QLD), and is benchmarked against a baseline CNN, a multilayer perceptron (MLP), an extended short-term memory network, and single-stage optimization variants. Across the three regions, the proposed approach achieves mean absolute percentage error (MAPE) values between 1.05% and 1.14%, representing an improvement of approximately 58% over the baseline CNN. Statistical robustness is examined using paired Wilcoxon signed-rank tests with Holm–Bonferroni correction on per-timestamp errors. Overall, the results indicate that combining random search with Bayesian optimization improves CNN forecasting accuracy across the three studied regions and provides a transparent tuning framework for future replication.
Co-Authors Ngoc Tran, Thanh Nguyen, Trung Dung Pham, Quang Minh Thi, Cam Vi Dao Tran, Thanh Ngoc Vu, Anh-Tuan