用于 EMS 的负荷监测时间序列维度扩展与迁移学习
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS
AI总结：
提出结合时间序列类视频维度扩展与资源感知深度学习的 EMS 负荷监测方法，并通过跨数据集迁移学习在少样本、训练效率和未见电器泛化上取得提升。
摘要：
能源管理系统（EMS）依赖（非）侵入式负荷监测（(N)ILM）来监测和管理电器，帮助居民提高能源效率，从而更加节俭。由于最具潜力的 (N)ILM 机器学习方案是在相对有限的数据上训练和评估的，其鲁棒性和迁移潜力尚未得到充分理解。本文提出一种基于时间序列维度扩展和迁移学习的建筑 EMS 负荷监测新方法。我们在 5 个不同的低频数据集上进行了广泛评估。所提出的特征维度扩展采用类视频变换和资源感知深度学习架构，在包含 29 种电器的各数据集上取得了 0.88 的平均加权 F1 分数，并且与最先进的成像方法相比计算效率更高。在研究所提方法的跨数据集域内迁移学习时，我们发现：1）该方法取得 0.80 的平均加权 F1 分数，同时与非迁移方法相比，模型训练所需轮次减少 3 倍；2）仅使用 230 个数据样本即可达到 0.75 的 F1 分数；3）对于未见过的电器，我们的迁移方法在精度下降方面优于最先进方法，降幅最多可达 12 个百分点。
英文摘要：
Energy management systems (EMS) rely on (non)-intrusive load monitoring (N)ILM to monitor and manage appliances and help residents be more energy efficient and thus more frugal. The robustness as well as the transfer potential of the most promising machine learning solutions for (N)ILM is not yet fully understood as they are trained and evaluated on relatively limited data. In this paper, we propose a new approach for load monitoring in building EMS based on dimensionality expansion of time series and transfer learning. We perform an extensive evaluation on 5 different low-frequency datasets. The proposed feature dimensionality expansion using video-like transformation and resource-aware deep learning architecture achieves an average weighted F1 score of 0.88 across the datasets with 29 appliances and is computationally more efficient compared to the state-of-the-art imaging methods. Investigating the proposed method for cross-dataset intra-domain transfer learning, we find that 1) our method performs with an average weighted F1 score of 0.80 while requiring 3-times fewer epochs for model training compared to the non-transfer approach, 2) can achieve an F1 score of 0.75 with only 230 data samples, and 3) our transfer approach outperforms the state-of-the-art in precision drop by up to 12 percentage points for unseen appliances.
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