A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting
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 househol...
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/3/486 |
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| author | Yitao Zhao Jiahao Li Chuanxu Chen Quansheng Guan |
| author_facet | Yitao Zhao Jiahao Li Chuanxu Chen Quansheng Guan |
| author_sort | Yitao Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a40ca6dd9d2d4d66bfa9b2a533cb007b |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-a40ca6dd9d2d4d66bfa9b2a533cb007b2025-08-20T02:48:06ZengMDPI AGEnergies1996-10732025-01-0118348610.3390/en18030486A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load ForecastingYitao Zhao0Jiahao Li1Chuanxu Chen2Quansheng Guan3Yunnan Power Grid Co., Ltd., Kunming 650217, ChinaYunnan Power Grid Co., Ltd., Kunming 650217, ChinaSchool of Electronics and Information, South China University of Technology, Guangzhou 510641, ChinaSchool of Electronics and Information, South China University of Technology, Guangzhou 510641, ChinaWith 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.https://www.mdpi.com/1996-1073/18/3/486very-short-term load forecastingmulti-feature fusiondiffusion modelattention mechanismenergy management |
| spellingShingle | Yitao Zhao Jiahao Li Chuanxu Chen Quansheng Guan A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting Energies very-short-term load forecasting multi-feature fusion diffusion model attention mechanism energy management |
| title | A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting |
| title_full | A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting |
| title_fullStr | A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting |
| title_full_unstemmed | A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting |
| title_short | A Diffusion–Attention-Enhanced Temporal (DATE-TM) Model: A Multi-Feature-Driven Model for Very-Short-Term Household Load Forecasting |
| title_sort | diffusion attention enhanced temporal date tm model a multi feature driven model for very short term household load forecasting |
| topic | very-short-term load forecasting multi-feature fusion diffusion model attention mechanism energy management |
| url | https://www.mdpi.com/1996-1073/18/3/486 |
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