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All Journal Bulletin of Electrical Engineering and Informatics
Tuan Anh Nguyen
Industrial University of Ho Chi Minh City
Electrical & Electronics Engineering

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A new grid search algorithm based on XGBoost model for load forecasting Ngoc Thanh Tran; Thanh Thi Giang Tran; Tuan Anh Nguyen; Minh Binh Lam
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5016

Abstract

XGBoost is a highly effective and widely used machine learning model and its hyperparameters take an important role on the performance of the model. This paper presents a new grid search (GS) algorithm for obtaining optimal hyperparameters of the XGBoost model based on the median values of their error loss. A benchmark method used to evaluate the proposed and original GS algorithms is introduced. Datasets with measured daily electricity demand load values of Ho Chi Minh City, Vietnam and Tasmania state, Australia are analyzed for the performance of both algorithms. The error metrics, mean squared errors (MSEs), of the proposed algorithm are found to be 2,282 MW and 501 MW that are smaller than those of original algorithms, which are 2,424 MW and 537 MW in case of Ho Chi Minh City and Tasmania state, respectively. These results then verify the accuracy of the proposed algorithm.
Two-stage random search–Bayesian optimization for CNN-based short-term load forecasting Tuan Anh Nguyen; Thanh Ngoc Tran
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 Minh Binh Lam Ngoc Thanh Tran Thanh Ngoc Tran Thanh Thi Giang Tran