Explainable attention-based neural network for load forecasting in super smart grids using socioeconomic and power consumption data

Super Smart Grids (SSG) aim to provide large-scale, multi-zonal electricity access while dynamically balancing supply and demand. However, their implementation faces multidisciplinary challenges that range from ensuring grid stability to avoiding structural injustices in their design. Existing load forecasting approaches are unsuitable for SSG planning or deployment due to an over-reliance on regional specificity, time-series data, and a lack of social understanding. This paper proposes a hybrid approach to load forecasting that combines time-series power consumption and socioeconomic metrics, coupled with a novel deep learning algorithm that integrates Artificial Neural Network (ANN) and Luong's Attention Mechanism (LAM). First, two parallel ANNs are used to extract the main features of load demand behavior. Then, LAM fuses both ANNs using an attention score function that dynamically selects the most relevant characteristics per sample to improve the generalization abilities of the model. The SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) frameworks then interpret this algorithm to thoroughly comprehend its load forecast decision-making. Evaluated on 92 suburban zones in Australia, the proposed method achieves a Mean Absolute Percentage Error (MAPE) of 1.78%, outperforming Bidirectional Long-Short Term Memory (BiLSTM), Long-Short Term Memory (LSTM) and Recurrent Neural Network (RNN) when socioeconomic metrics are present during training. By merging high-resolution forecasting with socioeconomic awareness, this approach enhances demand and supply management, optimizes pricing strategies, and ensures equitable energy distribution-critical requirements for SSG deployment.