This paper presents a hybrid model for medium- and long-term electricity demand forecasting, developed with the aim of achieving accurate forecasts on horizons ranging from a few weeks to several months. Univariate deep models show a significant drop in performance at higher time horizons and are unable to adequately model long-term nonlinearities and changes in electricity demand. To address these limitations, an approach is proposed that combines deterministic time-series decomposition, residual modeling using deep dilated convolutional networks (TitanResNet), and additional residual correction using the LightGBM model. In addition, the meta-model integrates two complementary components in order to achieve greater robustness and stability on long horizons.The proposed approach achieves strong long-horizon forecasting performance on the investigated dataset, especially on horizons from 720 to 2160 h, where MAPE ~4% and R2>0.95 under the adopted chronological protocol. By using climatological averages to generate future features, the framework preserves causality and remains applicable when true future meteorological measurements are unavailable. The results indicate that residual learning, deep convolutions, tree-based correction, and calibration-stage fusion provide a robust forecasting framework, while the broader generality of the approach requires validation on additional datasets.
