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Short-term fast forecasting based on family behavior pattern recognition for small-scale users load

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Abstract

Household electricity consumption has been rising gradually with the improvement of living standards. Making short-term load forecasting at the small-scale users plays an increasingly important role in the future power network planning and operation. To meet the efficiency of the dispatching system and the demand of human daily power consumption, an optimal forecasting model Attention-CNN-GRU of small-scale users load at various periods of the day based on family behavior pattern recognition is proposed in this study. The low-level data information (smart meter data) is used to build the high-level model (small-scale users load). Attention mechanism and convolutional neural networks (CNN) can further enhance the prediction accuracy of gated recurrent unit (GRU) and notably shorten its prediction time. The recognition of family behavior patterns can be achieved through the users’ smart meter data, and users are aggregated into K categories. The results of optimal K category prediction under the family behavior model are summarized as the final prediction outcome. This idea framework is tested on real users’ smart meter data, and its performance is comprehensively compared with different benchmarks. The results present strong compatibility in the small-scale users load forecasting model at various periods of the day and swift short-term prediction of users load compared to other prediction models. The time is shortened by 1/4 compared with the GRU/LSTM model. Furthermore, the accuracy is improved to 92.06% (MAPE is 7.94%).

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Data availability

The datasets generated during and analysed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to thank the editor and anonymous reviewers for their valuable advice that is very helpful for improving our paper. This work was supported by the National Natural Science Foundation of China under Grant 61773314, the Shaanxi Provincial Natural Science Basic Research Program under Grant 2019JZ-11, and the Scientific Research Project of Education Department of Shaanxi Provincial Government under Grant 19JC011.

Funding

This work was supported by the National Natural Science Foundation of China under Grant 61773314, the Shaanxi Provincial Natural Science Basic Research Program under Grant 2019JZ-11, and the Scientific Research Project of Education Department of Shaanxi Provincial Government under Grant 19JC011.

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Authors

Contributions

XC and LW developed the idea of the study, participated in its design and coordination and helped to draft the manuscript. QY and PZ contributed to the acquisition and interpretation of data. XW and LW provided critical review and substantially revised the manuscript. All authors commented on previous versions of the manuscript and all authors read and approved the final manuscript.

Corresponding author

Correspondence to Lei Wang.

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All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

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The code that support the findings of this study are available from the corresponding author upon reasonable request.

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Cheng, X., Wang, L., Zhang, P. et al. Short-term fast forecasting based on family behavior pattern recognition for small-scale users load. Cluster Comput 25, 2107–2123 (2022). https://doi.org/10.1007/s10586-021-03362-9

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  • DOI: https://doi.org/10.1007/s10586-021-03362-9

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