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All Journal Buletin Ilmiah Sarjana Teknik Elektro
Nguyen Anh Tuan
Industrial University of Ho Chi Minh City (IUH)
Electrical & Electronics Engineering

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Hybrid Stacking of Multilayer Perceptron, Convolutional Neural Network, and Light Gradient Boosting Machine for Short-Term Load Forecasting Trung Dung Nguyen; Nguyen Anh Tuan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14410

Abstract

Short-term load forecasting (STLF) is essential for scheduling, dispatch, and demand-side management. Real-world load series exhibit rapid local fluctuations and calendar or exogenous influences that challenge single-model approaches. This study proposes a hybrid stacking framework combining a Multilayer Perceptron (MLP), a 1-D Convolutional Neural Network (CNN), and a Light Gradient Boosting Machine (LightGBM), integrated through a ridge-regression meta-learner. The CNN extracts local temporal patterns from sliding windows of the load series, and the MLP processes tabular features such as lags, rolling statistics, and calendar/holiday indicators. At the same time, LightGBM captures nonlinear interactions in the same feature space. Base learners are trained using a rolling TimeSeriesSplit to avoid temporal leakage, and their out-of-fold predictions are used as inputs for the meta-learner. Early stopping regularizes the neural models. Experimental backtests on Queensland electricity demand data (89,136 half-hourly samples) demonstrate that the stacked model achieves markedly lower forecasting errors, with MAPE ≈ 0.81%, corresponding to a 24% reduction compared to CNN (MAPE ≈ 1.07%) and a 32% reduction compared to MLP (MAPE ≈ 1.19%). Regarding runtime, LightGBM is the fastest (25s) but least accurate, while the stacked model requires longer computation (2488s) yet delivers the most reliable forecasts. Overall, the proposed framework balances accuracy and robustness, and it is modular, reproducible, and extensible to additional exogenous inputs or base learners.
Hybrid Bayesian–Hyperband Optimization of a One-Dimensional Convolutional Neural Network for Short-Term Load Forecasting Trung Dung Nguyen; Nguyen Anh Tuan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.14824

Abstract

Short-term load forecasting (STLF) plays a crucial role in power system operation and electricity market planning. However, the forecasting accuracy of convolutional neural network (CNN) models strongly depends on properly tuned hyperparameters such as filter numbers, batch size, and loss function, while exhaustive tuning is computationally expensive. This paper proposes a one-dimensional CNN (1D-CNN) for STLF whose key hyperparameters are optimized by a hybrid Bayesian Optimization–Hyperband framework (BO-TPE-HB). The framework combines the Tree-structured Parzen Estimator (TPE), which guides the search towards promising regions of the hyperparameter space, with Hyperband’s multi-fidelity early-stopping strategy to terminate weak configurations early and save computation. The proposed approach is evaluated using half-hourly electricity load data from the Australian Energy Market Operator (AEMO) for New South Wales (NSW) and Victoria (VIC) from 2009 to 2014. Using a rolling-origin time-series cross-validation scheme on the training set and a chronologically separated hold-out test set, BO-TPE-HB explores 100 candidate configurations of a 4-layer 1D-CNN with a sliding window of 48 time steps. Model performance is assessed using MAE, MSE, RMSE, MAPE, and total hyperparameter optimization runtime. Experimental results show that the BO-TPE-HB–tuned CNN achieves test MAPE of 1.350% (MAE 105.98 MW, RMSE 143.71 MW) for NSW and 1.615% (MAE 89.13 MW, RMSE 124.25 MW) for VIC, outperforming Random Search, standalone TPE, and standalone Hyperband in terms of both prediction accuracy and computational efficiency, while requiring substantially less tuning time than plain Bayesian Optimization. These findings highlight that combining probabilistic search with resource-efficient early stopping provides a practical and reproducible way to enhance CNN-based STLF, and can be extended to multivariate inputs or other deep learning forecasting architectures in future work.
Co-Authors Trung Dung Nguyen