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Optimized wavelet-denoised temporal convolutional network for multi-step ahead predictions in power systems
Publication . Buratto, William Gouvea; Gonzalez, Gabriel Villarrubia; Yamaguchi, Cristina Keiko; Nied, Ademir; Yow, Kin-Choong; Stefenon, Stefano Frizzo
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.
2026-08-17Artigo de investigação Acesso aberto Ver mais