融合PCA与ICA特征的轻量模型，提升多设备同时运行时的用电分解准确率。
Fusion-ResNet: A Lightweight multi-label NILM Model Using PCA-ICA Feature Fusion
- 用PCA与ICA联合提取特征，增强不同电器的区分能力。
- 在15个并发设备下仍保持高准确率，平均F1得分优于现有方法。
- 模型轻量化，训练与推理速度快，适合实际部署场景。
非侵入式负载监测（NILM）是一种利用数据驱动算法将家庭总用电量分解为单个电器用电量的先进技术。然而真实应用中仍面临过拟合、模型泛化能力差以及同时运行大量电器难以分解等问题。本文提出一种端到端的NILM分类框架，包含高频标注数据、特征提取方法和轻量神经网络。其中引入一种融合独立成分分析（ICA）与主成分分析（PCA）特征的新方法，并设计轻量级多标签分类模型（Fusion-ResNet）。该基于特征的模型在不同电器上平均及整体F1得分均高于当前最优方法，且训练与推理时间显著减少。最后评估了模型在不同并发设备数量下的表现，结果表明：Fusion-ResNet在最多15个设备同时运行的复杂条件下仍具有较强鲁棒性。
原文摘要 · Abstract (English)
Non-intrusive load monitoring (NILM) is an advanced load monitoring technique that uses data-driven algorithms to disaggregate the total power consumption of a household into the consumption of individual appliances. However, real-world NILM deployment still faces major challenges, including overfitting, low model generalization, and disaggregating a large number of appliances operating at the same time. To address these challenges, this work proposes an end-to-end framework for the NILM classification task, which consists of high-frequency labeled data, a feature extraction method, and a lightweight neural network. Within this framework, we introduce a novel feature extraction method that fuses Independent Component Analysis (ICA) and Principal Component Analysis (PCA) features. Moreover, we propose a lightweight architecture for multi-label NILM classification (Fusion-ResNet). The proposed feature-based model achieves a higher $F1$ score on average and across different appliances compared to state-of-the-art NILM classifiers while minimizing the training and inference time. Finally, we assessed the performance of our model against baselines with a varying number of simultaneously active devices. Results demonstrate that Fusion-ResNet is relatively robust to stress conditions with up to 15 concurrently active appliances.
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