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Percorrer por autor "Coelho, Leandro Dos Santos"

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  • Multi-scale convolutional Chebyshev Kolmogorov-Arnold networks using conformal prediction for photovoltaic power forecasting
    Publication . Takara, Lucas de Azevedo; Souza, Rodrigo Clemente Thom de; Mariani, Viviana Cocco; Coelho, Leandro Dos Santos; Stefenon, Stefano Frizzo; Stefenon, Stefano Frizzo; IEEE
    Accurate photovoltaic (PV) power forecasting remains challenging due to nonlinear dynamics, rapid weather variations, and multi-scale temporal dependencies. This paper proposes ConvChebyKAN, a hybrid deep learning architecture that integrates multi-scale causal convolutions, Chebyshev polynomial parameterized Kolmogorov Arnold networks (KANs), and conformal prediction to improve both deterministic and probabilistic forecasting performance. The multi-scale convolutional module captures short-term fluctuations and long-term temporal dependencies, while the Chebyshev-based KAN improves nonlinear representation capability and numerical stability. A multi-objective hyperparameter optimization strategy based on Pareto efficiency and TOPSIS ranking is used to jointly optimize forecasting accuracy, prediction interval sharpness, and coverage reliability. The model is evaluated using real photovoltaic generation and meteorological data from four seasonal datasets and forecasting horizons from 1 to 48 hours ahead. ConvChebyKAN reaches an MAE of 149.13 kWh, RMSE of 227.34 kWh, R2 of 98.2%, and sMAPE of 6.21%, outperforming KAN and other machine learning models. These results show that the proposed architecture effectively captures multi-scale temporal patterns while providing reliable uncertainty estimates, supporting PV forecasting applications in power system operation and energy management.
    2026Artigo de investigação Acesso aberto Ver mais
  • Spatiotemporal wind energy forecasting: a comprehensive survey and a deep equilibrium-based case study with stemGNN
    Publication . Aquino, Luiza Scapinello; Seman, Laio Oriel; Mariani, Viviana Cocco; Coelho, Leandro Dos Santos; Stefenon, Stefano Frizzo; González, Gabriel Villarrubia
    Accurate 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.
    2025-07-24Artigo de investigação Acesso aberto Ver mais