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A k-nearest neighbor attentive deep autoregressive network for electricity consumption prediction

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Abstract

Electricity is vital in daily life and crucial for sustainable economic development. Accurate forecasting of energy consumption ensures efficient electricity system operation and supports strategic decision-making for energy distribution. Current time-series methods neglect the influence of neighboring regions’ electricity consumption and the varying impact levels caused by multiple factors on the target area. Therefore, we propose the k-nearest neighbor attentive deep autoregressive network (KNNA-DeepAR) model, which combines a k-nearest neighbor approach with an attentive deep autoregressive network, to achieve precise short-term electricity consumption predictions. By extracting informative features from historical time-series data, we incorporate electricity consumption data from the k regions closest to the target area as additional variables. Leveraging the attention mechanism, we assign varying weights to each variable to capture their interdependencies. Experimental results on a public dataset of electricity loads in fourteen U.S. regions demonstrate the superiority of our model. Compared to state-of-the-art time-series models, our model achieves higher predictive accuracy and exhibits significant potential as an effective approach for accurately predicting electricity consumption and other time-series tasks.

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Data availability

The datasets generated during and/or analysed during the current study are available in the Kaggle repository [https://www.kaggle.com/datasets/robikscube/hourly-energy-consumption?select=pjm_hourly_est.csv].

Notes

  1. https://www.kaggle.com/datasets/robikscube/hourly-energy-consumption?select=pjm_hourly_est.csv.

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Funding

This work is supported by the National Natural Science Foundation of China, “Science and Technology Innovation Action Plan” Shanghai Natural Science Foundation (Grant no. 62102241, no. 23ZR1425400).

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Author notes

  1. Xihe Qiu, Yajun Ru and Xiaoyu Tan have contributed equally to this work.

Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China

    Xihe Qiu, Yajun Ru & Jue Chen

  2. Department of Mechanical Engineering, University of Shanghai for Science and Technology, Jungong Road No. 516, Shanghai, 117575, China

    Bin Chen

  3. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, No. 333 Longteng Road, Shanghai, 201620, China

    Yun Guo

  4. INF Technology (Shanghai) Co., Ltd, Shanghai, 201210, China

    Xiaoyu Tan

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  1. Xihe Qiu
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  4. Jue Chen
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  5. Bin Chen
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by XQ, YR, JC, BC and YG. The first draft of the manuscript was written by XQ and XT all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yun Guo.

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Qiu, X., Ru, Y., Tan, X. et al. A k-nearest neighbor attentive deep autoregressive network for electricity consumption prediction. Int. J. Mach. Learn. & Cyber. 15, 1201–1212 (2024). https://doi.org/10.1007/s13042-023-01963-x

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