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TS2ARCformer: A Multi-Dimensional Time Series Forecasting Framework for Short-Term Load Prediction

Author

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  • Songjiang Li

    (College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China)

  • Wenxin Zhang

    (College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China)

  • Peng Wang

    (College of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China
    Changchun University of Science and Technology Chongqing Research Institute, Chongqing 401120, China)

Abstract

Accurately predicting power load is a pressing concern that requires immediate attention. Short-term load prediction plays a crucial role in ensuring the secure operation and analysis of power systems. However, existing research studies have limited capability in extracting the mutual relationships of multivariate features in multivariate time series data. To address these limitations, we propose a multi-dimensional time series forecasting framework called TS2ARCformer. The TS2ARCformer framework incorporates the TS2Vec layer for contextual encoding and utilizes the Transformer model for prediction. This combination effectively captures the multi-dimensional features of the data. Additionally, TS2ARCformer introduces a Cross-Dimensional-Self-Attention module, which leverages interactions across channels and temporal dimensions to enhance the extraction of multivariate features. Furthermore, TS2ARCformer leverage a traditional autoregressive component to overcome the issue of deep learning models being insensitive to input scale. This also enhances the model’s ability to extract linear features. Experimental results on two publicly available power load datasets demonstrate significant improvements in prediction accuracy compared to baseline models, with reductions of 43.2% and 37.8% in the aspect of mean absolute percentage error (MAPE) for dataset area1 and area2, respectively. These findings have important implications for the accurate prediction of power load and the optimization of power system operation and analysis.

Suggested Citation

  • Songjiang Li & Wenxin Zhang & Peng Wang, 2023. "TS2ARCformer: A Multi-Dimensional Time Series Forecasting Framework for Short-Term Load Prediction," Energies, MDPI, vol. 16(15), pages 1-22, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5825-:d:1211358
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    References listed on IDEAS

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    6. Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Prediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Approach," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
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    Cited by:

    1. Vasileios Laitsos & Georgios Vontzos & Paschalis Paraschoudis & Eleftherios Tsampasis & Dimitrios Bargiotas & Lefteri H. Tsoukalas, 2024. "The State of the Art Electricity Load and Price Forecasting for the Modern Wholesale Electricity Market," Energies, MDPI, vol. 17(22), pages 1-37, November.
    2. Fatma Mazen Ali Mazen & Yomna Shaker & Rania Ahmed Abul Seoud, 2023. "Forecasting of Solar Power Using GRU–Temporal Fusion Transformer Model and DILATE Loss Function," Energies, MDPI, vol. 16(24), pages 1-24, December.

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