Spatio-temporal analysis of power demand in smart grid based on stacked autoencoder

Hai Li; Jianfeng Feng; Huangjing Gu; Xiangyang Xue; Nannan Yan; Yu Cao; Haosheng Lv; Xiaodi Wang; Tianyu Yang

doi:10.1117/12.3050488

12 December 2024 Spatio-temporal analysis of power demand in smart grid based on stacked autoencoder

Hai Li, Jianfeng Feng, Huangjing Gu, Xiangyang Xue, Nannan Yan, Yu Cao, Haosheng Lv, Xiaodi Wang, Tianyu Yang

Proceedings Volume 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024); 134191T (2024) https://doi.org/10.1117/12.3050488
Event: Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 2024, Lhasa, China

Abstract

Energy demand in modern cities is becoming more and more complex. Knowing its pattern can help managers make proper decisions. However, finding the pattern of large-scale spatial coverage fields, such as a whole city, is not easy. In this paper, a data-driven stacked autoencoder-based framework is proposed to capture the spatio-temporal pattern for electricity consumption. The data can be organized as a one-time series and the geography coverage of the analysis can be determined by the collected data. An experiment with collected data proves the feasibility of the framework.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hai Li, Jianfeng Feng, Huangjing Gu, Xiangyang Xue, Nannan Yan, Yu Cao, Haosheng Lv, Xiaodi Wang, and Tianyu Yang "Spatio-temporal analysis of power demand in smart grid based on stacked autoencoder", Proc. SPIE 13419, Tenth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2024), 134191T (12 December 2024); https://doi.org/10.1117/12.3050488

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