Data-driven power load forecasting with neural attention mechanism

Jun Yan; Ning Ma; Jixuan Huang; Yi Wu; Siyu Liao; Peng Zhu; Dawei Cheng

doi:10.1117/12.3051636

18 November 2024 Data-driven power load forecasting with neural attention mechanism

Jun Yan, Ning Ma, Jixuan Huang, Yi Wu, Siyu Liao, Peng Zhu, Dawei Cheng

Author Affiliations +

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134033G (2024) https://doi.org/10.1117/12.3051636
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

With the advancement of industrialization and the development of the social economy, electric power resources have become an essential guarantee for ensuring the efficient operation of society. As a critical implementation step in the design and development of power systems, power load forecasting not only ensures the safe and stable operation of power systems but also assists in accomplishing reasonable power distribution tasks. It has significant technical and economic importance. However, existing research on power load forecasting primarily relies on expert systems or general time series forecasting methods, which seldom consider the differences between power load data and other time series data. This presents difficulties in effectively leveraging the spatiotemporal attributes of power load data for forecasting purposes. To tackle this challenge, this paper introduces a data-driven model for power load prediction utilizing the attention mechanism. Firstly, the model incorporates multi-source heterogeneous data, deeply exploring the spatiotemporal correlations of load user behavior data. Secondly, a covariate dimensionality reduction module based on residual neural networks is designed, significantly improving the model's computational efficiency. By constructing the Fourier transform, the model can effectively extract and embed the periodic characteristics of power data. The model is tested on a regional dataset and three public datasets. The findings indicate that the proposed approach surpasses baseline models across all evaluation metrics, offering dependable predictive support for the stable functioning of power systems.

Citation Download Citation

Jun Yan, Ning Ma, Jixuan Huang, Yi Wu, Siyu Liao, Peng Zhu, and Dawei Cheng "Data-driven power load forecasting with neural attention mechanism", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134033G (18 November 2024); https://doi.org/10.1117/12.3051636

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KEYWORDS

Data modeling

Fourier transforms

Autoregressive models

Power grids

Convolution

Neural networks

Transformers

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