论文标题
电器识别的表示学习：与古典机器学习的比较
Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning
论文作者
论文摘要
非侵入性负载监控（NILM）旨在借助信号处理和机器学习算法从汇总的消耗量测量中检索能源消耗和态度信息。用深层神经网络的表示学习成功地应用于几个相关学科。表示学习的主要优点在于，用原始数据格式的许多表示形式替换了专家驱动的手工制作的特征提取。在本文中，我们展示了如何改善尼尔姆加工链，复杂性降低并使用最近的深度学习算法进行设计。在基于事件的设备识别方法的基础上，我们评估了七个不同的分类模型：基于手工制作的特征提取的经典机器学习方法，三种不同的深神经网络体系结构，用于在原始波形数据上进行自动化特征提取，以及三种用于原始数据处理的基线方法。我们评估了两个大规模能源消耗数据集的所有方法，其中有50,000多个44个设备。我们表明，通过使用深度学习，我们能够以0.75和0.86的F-SCORE识别最先进的经典机器学习方法的性能，而经典方法的表现为0.69和0.87。
Non-intrusive load monitoring (NILM) aims at energy consumption and appliance state information retrieval from aggregated consumption measurements, with the help of signal processing and machine learning algorithms. Representation learning with deep neural networks is successfully applied to several related disciplines. The main advantage of representation learning lies in replacing an expert-driven, hand-crafted feature extraction with hierarchical learning from many representations in raw data format. In this paper, we show how the NILM processing-chain can be improved, reduced in complexity and alternatively designed with recent deep learning algorithms. On the basis of an event-based appliance recognition approach, we evaluate seven different classification models: a classical machine learning approach that is based on a hand-crafted feature extraction, three different deep neural network architectures for automated feature extraction on raw waveform data, as well as three baseline approaches for raw data processing. We evaluate all approaches on two large-scale energy consumption datasets with more than 50,000 events of 44 appliances. We show that with the use of deep learning, we are able to reach and surpass the performance of the state-of-the-art classical machine learning approach for appliance recognition with an F-Score of 0.75 and 0.86 compared to 0.69 and 0.87 of the classical approach.
