Abstract
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.
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Data availability
The datasets generated during and/or analyzed during the current study are not publicly available but are available from the corresponding author on reasonable request.
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Funding
This research was funded by Science &Technology Project of Sichuan Province Electric Power Company, grant number 52199723002T.
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Conceptualization: Hongli Liu, Yang Wei; Methodology: Han Zhang; Software: Yang Wang; Validation: Jie Zhang； Investigation: Hongli Liu, Yang Wei, Zhongqiang Luo; Writing-Original Draft: Hongli Liu, Yang Wei; Writing-Review & Editing: All of the authors. All authors have read and agreed to the published version of the manuscript.
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Liu, H., Wei, Y., Zhang, H. et al. A hybrid LSTM-transformer model for short-term urban electricity load forecasting: a two-city case study. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47905-9
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DOI: https://doi.org/10.1038/s41598-026-47905-9
