Percorrer por autor "Yow, Kin-Choong"
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- Fourier-enhanced sequence-to-sequence latent graph neural networks for multi-node spatiotemporal forecasting in a hydroelectric reservoirPublication . Seman, Laio Oriel; Stefenon, Stefano Frizzo; Yow, Kin-Choong; Coelho, Leandro dos Santos; Mariani, Viviana CoccoThis paper presents a Fourier-enhanced dynamic sequence-to-sequence latent graph neural network (Seq2SeqLatentGNN), a deep learning architecture for multi-node spatiotemporal forecasting in hydroelectric reservoir systems. The model integrates three key components: (i) a custom Fourier layer that analyzes global temporal patterns through frequency-domain transformations, (ii) a latent correlation graph convolutional network that infers relational structures between monitoring stations without requiring predefined adjacency matrices, and (iii) an attention-based sequence-to-sequence model that processes temporal dependencies while enabling multi-step forecasting. The architecture simultaneously learns graph structure and forecasting tasks, adapting to changing spatial relationships between reservoir nodes. The proposed architecture was evaluated using a comprehensive dataset derived from 19 interconnected hydroelectric reservoirs located in southern Brazil. The dataset encompasses multiple years of high-resolution (hourly) measurements, including reservoir water levels, inflow and outflow rates, precipitation records, and energy production metrics. Experimental results demonstrate that Seq2SeqLatentGNN achieves superior performance compared to conventional statistical models and contemporary machine learning methods, as measured by standard error metrics. Analysis of the learned latent correlations reveals meaningful spatial dependencies that align with hydrological principles. The model exhibits consistent performance across varying temporal patterns, adapts to regime transitions, and captures both periodic and nonstationary dynamics. The proposed architecture contributes to spatiotemporal forecasting by combining spectral processing, dynamic graph learning, and sequence modeling in a unified framework applicable to systems with evolving connectivity patterns.
- Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive modelsPublication . Seman, Laio Oriel; Stefenon, Stefano Frizzo; Yow, Kin-Choong; Coelho, Leandro dos Santos; Mariani, Viviana CoccoAccurate short and medium-term forecasting is important for mitigating uncertainty and enabling efficient energy grid management. While traditional machine learning and deep learning models offer improved accuracy, they often lack interpretability. To address these limitations, this study proposes a hybrid forecasting framework, called FNO-BiLSTM-NAM, that combines a Fourier Neural Operator (FNO) to extract spectral–temporal features, a Bidirectional Long Short-Term Memory (BiLSTM) network to model sequential dependencies, and a Neural Additive Model (NAM) to quantify feature-wise contributions. The model incorporates multi-scenario forecasting to support energy operators under different uncertainty levels. Experiments conducted on a dataset from a 5 MW PhotoVoltaic (PV) plant demonstrate the superiority of the model. For a 6-hour forecast horizon, the proposed FNO-BiLSTM-NAM model achieved a mean absolute error of 0.0712 and mean squared error of 0.0092, outperforming benchmark models across short- to medium-term horizons. Furthermore, the spectral analysis of the FNO revealed low-pass filtering behavior, highlighting the ability of the model to suppress high-frequency noise. Comparative experiments with five machine and deep learning baseline models confirm the robustness and generalization capacity of the framework. These results underscore the potential of the proposed model for enhancing PV energy forecasting accuracy while maintaining transparency across dynamic operating conditions.
- Sparse mixture of experts enhanced transformer architecture for short-term hydroelectric reservoir volume predictionPublication . Seman, Laio Oriel; Yow, Kin-Choong; Stefenon, Stefano FrizzoIn hydroelectric-based systems, effective energy generation planning relies heavily on precise forecasting of reservoir water levels. This paper proposes a novel hybrid forecasting framework that integrates multiple preprocessing strategies with a sparse Mixture of Experts enhanced Transformer architecture for short-term reservoir volume prediction. When evaluated on 19 interconnected reservoirs across two major river basins in southern Brazil using real operational data from the Brazilian National System Operator, the proposed model achieves a mean squared error of 0.062 and a mean absolute error of 0.145. Comprehensive benchmarking against 18 state-of-the-art deep learning methods demonstrates that the proposed approach significantly outperforms existing methods while maintaining computational efficiency through sparse expert routing. Our results confirm that combining diverse preprocessing strategies with conditional computation mechanisms provides superior forecasting accuracy for reservoir management in hydroelectric power systems.