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

Songjiang Li, Wenxin Zhang and Peng Wang ()
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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

Energies, 2023, vol. 16, issue 15, 1-22

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.

Keywords: multivariate time series; load prediction; TS2Vec; transformer; attention (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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