TETRANSFORMER ULTRASHORT TERM LOAD POWER FORECASTING CONSIDERING SIMILAR DAYS AND ERROR CORRECTION

Li Lianbing; Gao Yibo; Wu Weiqiang; Wei Yuchong; Dai Liangliang; Gao Guoqiang

doi:10.19912/j.0254-0096.tynxb.2024-1608

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 301-312. DOI: 10.19912/j.0254-0096.tynxb.2024-1608

TETRANSFORMER ULTRASHORT TERM LOAD POWER FORECASTING CONSIDERING SIMILAR DAYS AND ERROR CORRECTION

Li Lianbing¹, Gao Yibo¹, Wu Weiqiang², Wei Yuchong², Dai Liangliang³, Gao Guoqiang¹

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Abstract

In order to further improve the prediction accuracy of ultra-short-term power load and enhance the extraction ability of power load temporal features, a temporal enhancement Transformer （TETransformer） ultra-short-term power load prediction method considering similar days and error correction is proposed. First, the meteorologically similar days are selected using gray relational analysis, and then, the local temporal enhancement attention mechanism is constructed on the basis of the Transformer model, and the temporal convolution is used to improve the local temporal feature perception ability of the attention mechanism, and to aggregate the relevant information of the adjacent area of the observation point; the traditional Transformer model is embedded with a temporal convolution layer, and the feature map is extended, in which the global information of the Transformer model is not available to the public. traditional Transformer model to extend the feature map and enhance the local time-series information extraction capability on the basis of the global information extraction of the Transformer model. Finally, the historical feature data and future meteorological data are input to TETransforemr, and the load power sequences of meteorologically similar days are input to LSTM, and the historical time-series features and similar day information are fused through the fully-connected layer, and the encoder-based error correction module is introduced to improve the model prediction accuracy. Through the multi-model comparison and ablation experiments, the prediction accuracies are improved, which proves that the proposed method can effectively enhance the extraction ability of power load and has certain application significance in the field of ultra-short-term power load.

Key words

load power forecasting / Transformer model / similar days selection / gray relational analysis / error correction / temporal convolution

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Li Lianbing, Gao Yibo, Wu Weiqiang, Wei Yuchong, Dai Liangliang, Gao Guoqiang. TETRANSFORMER ULTRASHORT TERM LOAD POWER FORECASTING CONSIDERING SIMILAR DAYS AND ERROR CORRECTION[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 301-312 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1608

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