Dual-Stream CNN-GRU Model with Spatial and Self-Attention for Power Forecasting in Electrical Automation
Abstract
Accurate power forecasting is critical for electrical automation within smart grids, enabling intelligent energy management to balance demand and supply. We propose a dual-stream hybrid model that processes multivariate time series data from the DKASC (solar generation) and IHEPC (household consumption) datasets through parallel Convolutional Neural Networks (CNNs) with spatial attention and Gated Recurrent Units (GRUs) with self-attention. This unified model captures cross-variable correlations (e.g., weather, power output) and long-term temporal dependencies to predict both generation and consumption, enhancing grid stability. Evaluated using RMSE, MAE, and MSE, our model achieves a 32–93% reduction in RMSE compared to baseline methods like RCC-LSTM and CNN-LSTM hybrids on DKASC and IHEPC datasets. Designed for integration with electrical automation systems, it offers superior accuracy, robustness, and efficiency, advancing smart grid operations.
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DOI: https://doi.org/10.31449/inf.v49i31.8377
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