A hybrid LSTM-transformer model for short-term urban electricity load forecasting: a two-city case study.

Accurate short-term urban power load forecasting is crucial for power system dispatching, peak load management, and data-driven urban energy planning. To improve the forecasting performance of multivariate load time series under complex temporal dependencies and external influencing factors, this paper proposes a hybrid LSTM-Transformer model, combining the temporal representation capabilities of Long Short-Term Memory (LSTM) networks with the global dependency modeling capabilities of Transformer encoders. Based on multi-source 15-minute time series data from two representative cities, the proposed model is evaluated on a short-term power load forecasting task and compared with baseline models including traditional LSTM and DE-LSTM. Experimental results show that, under a given evaluation protocol, the hybrid model achieves lower prediction errors and higher fitting accuracy. These results demonstrate that the proposed structure can effectively capture the local temporal dynamics and long-distance feature interactions in urban load sequences.