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PowerDiffuser: Collaborative Contrastive-Reconstruction Self-Supervised Learning for Robust Power Load Signal Representation
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AI
DOI：10.1109/TII.2025.3627498.png)
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Abstract
En 中文
The widespread deployment of smart meters has created significant opportunities for applying artificial intelligence technologies to power system tasks. However, the high cost of data annotation limits the effectiveness of traditional supervised learning in this domain, making self-supervised learning an attractive alternative. In this article, we propose PowerDiffuser, a novel self-supervised learning strategy tailored for power load signals. By leveraging a diffusion model framework, PowerDiffuser integrates two mainstream self-supervised paradigms, namely contrastive learning and reconstruction-based learning, which enables the model to effectively capture both periodic patterns and local features. To address the overfitting issues commonly observed in generic time-series feature extractors when applied to power load tasks, we design two modular spatiotemporal feature extractors specifically engineered to handle samples with varying complexity levels. In addition, we adapt the involution operator to better align with the unique characteristics of power load signals. Extensive experiments on the ISMCBT, ETTh and REDD datasets demonstrate that PowerDiffuser consistently outperforms both time-series general models and existing self-supervised learning strategies across diverse downstream power load tasks. Ablation studies further validate the contributions of the proposed modules and highlight the effectiveness of transforming 1-D load signals into 2-D periodicity-based representations as a preprocessing step.
Keywords:
Involution
power signal
self-supervised learning
spatiotemporal feature extractors
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