Examinando por Autor "Nguyen-Huy, Thong"

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A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction
(Elsevier, 2023) Ghimire, Sujan; Nguyen-Huy, Thong; AL-Musaylh, Mohanad S.; Deo, Ravinesh C.; Casillas-Pérez, David; Salcedo-Sanz, Sancho
Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem.
Novel deep hybrid model for electricity price prediction based on dual decomposition
(Elsevier, 2025-10-01) Ghimire, Sujan; Nguyen-Huy, Thong; Deo, Ravinesh C.; Casillas-Pérez, David; Ahmed, A.A. Masrur; Salcedo-Sanz, Sancho
Electricity price (𝐸𝑃 ) forecasting is vital for effective market operation, strategic planning, and risk management in deregulated energy systems. However, the inherent volatility and complexity of electricity prices, shaped by demand supply dynamics, weather variability, and regulatory interventions, pose substantial challenges to accurate prediction. This study introduces a novel hybrid framework designed to improve forecasting accuracy by leveraging both signal decomposition and deep learning techniques. Specifically, the method integrates Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) for noise reduction and feature extraction, followed by a Multi Resolution Convolution (MRC) layer and a Bidirectional Long Short Term Memory (BiLSTM) network to capture multiscale temporal patterns in electricity price data. The model is applied to half hourly electricity price data from South Australia spanning January 2018 to December 2022. Its performance is benchmarked against a suite of traditional and hybrid models using a comprehensive set of twelve evaluation metrics. The results reveal that the proposed hybrid model consistently outperforms all baselines across seasons and forecast horizons. Notably, during the spring period, it achieved a Normalized Root Mean Square Error of ≈ 4.87 %, a Mean Absolute Percentage Error of ≈ 12.09 %, and a Global Performance Index of ≈ 3.22. These improvements demonstrate the model’s ability to effectively handle the non-linear and nonstationary nature of 𝐸𝑃 . Overall, the findings underscore the potential of combining advanced decomposition methods with deep learning architectures to deliver more accurate and robust 𝐸𝑃 forecasts, thereby offering valuable support for decision making in complex and evolving energy markets.