Optimized Wavelet‐Denoised Temporal Convolutional Network for Multi‐Step Ahead Predictions in Power Systems
William Gouvêa Buratto, Gabriel Villarrubia Gonzalez, Cristina Keiko Yamaguchi, Ademir Nied, Kin‐Choong Yow, Stefano Frizzo StefenonABSTRACT
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.