A Parallel Processing Architecture for Long-Term Power Load Forecasting

The increasing complexity of power grids and integration of renewable energy sources necessitate accurate power load forecasting across multiple time horizons. While existing methods have advanced significantly, they often struggle with consistent performance across different prediction ranges, leading to suboptimal resource allocation. We propose MP-RWKV (Multi-Path Recurrent Weighted Key–Value), an enhanced architecture that builds upon RWKV-TS and addresses these challenges through parallel processing paths for temporal modeling. Our model maintains robust performance across both short-term and long-term forecasting scenarios through its context state mechanism and position-aware attention. Evaluated on extensive power load data, MP-RWKV demonstrates superior performance over state-of-the-art baselines, including Transformer-based models and LSTM variants. The model achieves the lowest Mean Absolute Error (MAE) across prediction horizons ranging from 24 h to 432 h, showing particular strength in maintaining consistent accuracy where traditional models deteriorate. Notably, MP-RWKV successfully balances immediate temporal correlations with extended dependencies, offering promising implications for power grid management and sustainable energy systems. The model’s stable performance across varying prediction horizons makes it particularly suitable for real-world power load forecasting applications.