Development of Temporal-Spatial Long Short-Term Memory with Attention Mechanism for Electricity Short-Term Load Forecasting in Bauchi Metropolis

Electricity short-term load forecasting (STLF) represents a critical operational imperative for modern power system economic dispatch, spinning reserve allocation, and grid stability. Within distribution networks, electricity demand exhibits intricate non-linear temporal dynamics alongside pronounced spatial correlations across interconnected feeder nodes. Conventional Long Short-Term Memory (LSTM) recurrent networks effectively model temporal sequences but fail to capture cross-feeder spatial dependencies and encounter representational bottlenecks over extended look-back horizons. This paper proposes a novel Temporal-Spatial Long Short-Term Memory architecture integrated with dual-stage attention mechanisms (TS-LSTM-Attn) for one-hour-ahead multi-output load forecasting across the eight 33 kV distribution feeders of Bauchi Metropolis, Nigeria (GRA/Palace, Works, Yankari, Nabordo, Ningi, Dass, Dabo Farm, and Gubi Dam). The architecture integrates an LSTM temporal recurrent encoder, four-head spatial self-attention across feeder nodes, residual connections with feed-forward layer normalization, temporal sequence attention over a 168-hour historical look-back window, and feeder-specific embeddings. Real-world hourly electricity load telemetry spanning 2023 to 2026 was synchronized with hourly meteorological regressors obtained from the Nigerian Meteorological Agency (NiMet) and engineered calendar variables. Evaluated under strict leakage-free chronological partitioning, the proposed TS-LSTM-Attn model achieved a mean feeder-level Mean Absolute Error (MAE) of 0.8240 kW across the eight distribution feeders, outperforming the conventional standard LSTM (1.1232 kW) by 26.63% and surpassing the competitive persistence forecasting benchmark (0.8394 kW) by 1.83%. At the aggregate system level, the proposed model attained a Total Bauchi MAE of 3.9444 kW compared to 4.3532 kW for the standard LSTM (a 9.39% improvement) and 4.0087 kW for persistence. Interpretability analysis revealed that the temporal attention layer concentrates over 92.32% of its representational weight on the immediate past three hours (t-1: 67.87%, t-2: 18.31%, t-3: 6.14%), while the learned spatial attention matrix reflects distributed multi-feeder interaction (~0.12–0.13) governed by shared 132/33 kV substation busbar dynamics. The study provides empirical proof that fusing spatiotemporal feature learning with dual-stage attention substantially enhances both forecasting accuracy and operational interpretability for utility dispatchers in developing distribution grids.