MRGNN: Multi-resolution graph neural network for electricity load forecasting

Electricity load is highly volatile and uncertain, which makes accurate forecasting very challenging but critical for effective power management and grid stability. Existing forecasting methods struggle to handle long-term time series, complex multi-scale temporal patterns, and noisy data, which significantly limits their effectiveness in real-world applications. To address these challenges, this paper proposes a novel Multi-Resolution Graph Neural Network (MRGNN) that integrates multi-scale patching and Frequency-enhanced Direct Forecast (FreDF) approach. MRGNN partitions input sequences into multi-resolution segments to capture both short-term fluctuations and long-term trends while leveraging graph neural networks to model spatial–temporal dependencies. Additionally, a frequency-enhanced loss function aligns predictions with ground truth in both time and frequency domains, mitigating label correlation issues. Experiments on real-world datasets demonstrate MRGNN’s superiority among all models, achieving top performance in 92 out of 96 test cases, particularly for long-term forecasting horizons. The results highlight MRGNN’s robustness and precision, offering practical value for optimizing power systems and advancing spatio-temporal forecasting research.