A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting

Yitao Zhao, Jiahao Li, Chuanxu Chen () and Quansheng Guan
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Yitao Zhao: Yunnan Power Grid Co., Ltd., Kunming 650217, China
Jiahao Li: Yunnan Power Grid Co., Ltd., Kunming 650217, China
Chuanxu Chen: School of Electronics and Information, South China University of Technology, Guangzhou 510641, China
Quansheng Guan: School of Electronics and Information, South China University of Technology, Guangzhou 510641, China

Energies, 2025, vol. 18, issue 3, 1-21

Abstract: With the proliferation of smart home devices and the ever-increasing demand for household energy management, very-short-term load forecasting (VSTLF) has become imperative for energy usage optimization, cost saving and for sustaining grid stability. Despite recent advancements, VSTLF in the household scenario still poses challenges. For instance, some characteristics (e.g., high-frequency, noisy and non-stationary) exacerbate the data processing and model training procedures, and the heterogeneity in household consumption patterns causes difficulties for models with the generalization capability. Further, the real-time data processing requirement calls for both the high forecasting accuracy and improved computational efficiency. Thus, we propose a diffusion–attention-enhanced temporal (DATE-TM) model with multi-feature fusion to address the above issues. First, the DATE-TM model could integrate residents’ electricity consumption patterns with climatic factors. Then, it extracts the temporal feature using an encoder and meanwhile models the data uncertainty through a diffusion model. Finally, the decoder, enhanced with the attention mechanism, creates the precise prediction for the household load forecasting. Experimental results reveal that DATE-TM significantly surpasses classical neural networks such as BiLSTM and DeepAR, especially in handling the data uncertainty and long-term dependency.

Keywords: very-short-term load forecasting; multi-feature fusion; diffusion model; attention mechanism; energy management (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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