Accurate long-sequence net load forecasting is essential for reliable grid operation and renewable integration

yet it remains challenging under quasi-periodicity

sharp weather-driven variability and long-range error accumulation. We propose HarmoNet

an end-to-end dual-domain architecture for long-horizon deterministic and interval forecasting. HarmoNet (i) encodes coupled low/high-frequency representations with multi-scale temporal signals

(ii) integrates local pattern modelling and global dependency learning via a hybrid convolutional-transformer block and (iii) aggregates horizon-wide representations to mitigate drift in long-range prediction. Uncertainty is estimated with quantile regression (10%–50%–90%). We evaluate HarmoNet on four-year hourly net-load datasets from Belgium

Bulgaria and Italy (2016–2019) derived from the Open Power System Data platform

using eight exogenous meteorological covariates

over horizons of 96/192/336/720 h. Relative to the strongest baseline per dataset-horizon setting

HarmoNet reduces MAE by 14.2% on average (up to 22.5% on Italy at 720 h) and achieves average reductions of 28.4% in the Winkler score and 13.0% in pinball loss. Under deployment-oriented stress tests spanning high-volatility

peak-spike and steep-ramp windows

HarmoNet attains the best deterministic accuracy in 27/36 windows and the best probabilistic performance in 32/36 windows

indicating robust and deployment-friendly long-horizon forecasting.