Journal Article

Attentive Dual‐Domain Modelling for Error‐Resilient Net Load Forecasting in Intermittent Renewable Power Systems

Li, Zhiyi, Wu, Bizhi

2026

Overview

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.

• Publisher: John Wiley & Sons, Inc
• Subject: Accuracy / Alternative energy sources / Datasets / Decomposition / Design / Efficiency / Forecasting / Modelling / Range errors / Representations / Statistical analysis