Percorrer por autor "Stefenon, Stefano Frizzo"
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- Enhanced random vector functional link networks with bayesian-based hyperparameter optimization for wind speed forecastingPublication . Seman, Laio Oriel ; Klaar, Anne Carolina Rodrigues ; Ribeiro, Matheus Henrique Dal Molin ; Stefenon, Stefano FrizzoAccurate short-term wind speed forecasting is essential for reliable and efficient wind energy integration. This paper introduces an enhanced Random Vector Functional Link (RVFL) network optimized through a Bayesian-based Neural Architecture Search (NAS) framework. The proposed RVFL-OptBayes model incorporates multi-scale feature generation, including kernel approximations, Nystr & ouml;m sampling, Fastfood transforms, wavelet scattering, and Neural Tangent Kernel embeddings with Principal Component Analysis (PCA)-aligned orthogonal initializations and spectral normalization to improve stability and feature diversity. Experiments were conducted on real-world Brazilian wind farm data to evaluate forecasting performance. Results show that RVFL-OptBayes outperforms conventional RVFL networks, deep learning models, and ensemble methods, achieving an R2 above 0.99. The proposed framework demonstrates that lightweight randomized architectures, when combined with principled hyperparameter search, can rival or surpass complex deep learning models for time-series forecasting. The findings suggest strong potential for practical deployment in renewable energy systems, offering accurate and computationally efficient wind speed predictions to support operational planning, grid stability, and smart energy management.
- 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.
- Spatiotemporal wind energy forecasting: a comprehensive survey and a deep equilibrium-based case study with stemGNNPublication . Aquino, Luiza Scapinello; Seman, Laio Oriel; Mariani, Viviana Cocco; Coelho, Leandro Dos Santos; Stefenon, Stefano Frizzo; González, Gabriel VillarrubiaAccurate spatiotemporal wind energy forecasting is essential for ensuring grid stability and maximizing the efficiency of renewable energy systems. This paper addresses the challenge of modeling the complex spatial and temporal dependencies inherent in wind power generation by presenting a comprehensive survey of existing spatiotemporal forecasting methods and introducing an innovative deep learning approach. The proposed model integrates a Graph Neural Network (GNN) to represent wind turbines as nodes within a graph, capturing spatial relationships, while a Deep Equilibrium Model (DEQ) enables equilibrium-based inference to handle highly nonlinear wind patterns. A Sequence-to-Sequence (Seq2Seq) architecture further manages temporal dependencies. The method was validated using a real-world dataset of wind power generation, outperforming baseline models across multiple forecast horizons and maintaining stable accuracy across short- and mid-term predictions. Results demonstrate that the proposed GNN with DEQ effectively models both spatial and temporal dynamics for Seq2Seq data, improving prediction accuracy while maintaining computational efficiency. This study highlights the potential of equilibrium-based spatiotemporal graph models for wind energy forecasting and provides a robust tool for better integration of wind power into modern power grids.