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Orientador(es)
Resumo(s)
Accurate forecasting of power generation is essential for ensuring the reliable and cost-efficient operation of modern electrical grids. This paper introduces an optimized wavelet temporal convolutional network (TCN) designed for multi-step-ahead prediction of thermal power generation in Brazil. The proposed model, called optimized-wavelet-TCN, integrates a discrete wavelet transform denoising stage to effectively filter high-frequency noise from operational data while preserving underlying signal trends, followed by a TCN optimized via a tree-structured Parzen estimator for hyperparameter tuning. Evaluated on real-world hourly data spanning multiple years, the optimized-wavelet-TCN achieves superior forecasting performance with a mean absolute percentage error of 10.2%, 7.24%, and 8.54% for 15, 30, and 60-step ahead predictions, respectively. The model significantly reduces computational time to only 1.54 s for a 30-step forecast, markedly outperforming state-of-the-art transformer-based and recurrent neural network baselines in both accuracy and efficiency.
Descrição
Palavras-chave
Demand forecasting Discrete wavelet transforms Time series Generation
Contexto Educativo
Citação
Buratto, W. G., Gonzalez, G. V., Yamaguchi, C. K., Nied, A., Yow, K. C., & Stefenon, S. F. (2026). Optimized wavelet-denoised temporal convolutional network for multi-step ahead predictions in power systems. IET Generation Transmission & Distribution, 20(1), 1-16. https://doi.org/10.1049/gtd2.70398
Editora
Willey