论文标题
IMG-NILM：使用能量热图的深度学习尼尔姆方法
IMG-NILM: A Deep learning NILM approach using energy heatmaps
论文作者
论文摘要
能量分解估计的单仪表可以逐一逐电消耗电能，从而衡量整个房屋的电力需求。与侵入性负载监测相比，尼尔姆（非侵入性负载监控）是低成本，易于部署和灵活的。在本文中，我们提出了一种新方法，即创建的IMG-NILM，该方法利用卷积神经网络（CNN）来分解表示为图像的电力数据。 IMG-NILM不是将电力数据作为时间序列处理的传统方法，而是将时间序列转变为热图，而较高的电力读数则将其描绘成“更热”的颜色。然后，在CNN中使用图像表示来检测来自聚合数据的设备的签名。 IMG-NILM强大而灵活，在各种类型的电器上的性能一致；包括单个和多个状态。它在单个房屋内的英国数据集中达到了高达93％的测试准确性，那里有大量设备。在从不同房屋中收集电力数据的更具挑战性的环境中，IMG-NILM的平均准确度也非常好，为85％。
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Compared with intrusive load monitoring, NILM (Non-intrusive load monitoring) is low cost, easy to deploy, and flexible. In this paper, we propose a new method, coined IMG-NILM, that utilises convolutional neural networks (CNN) to disaggregate electricity data represented as images. Instead of the traditional approach of dealing with electricity data as time series, IMG-NILM transforms time series into heatmaps with higher electricity readings portrayed as 'hotter' colours. The image representation is then used in CNN to detect the signature of an appliance from aggregated data. IMG-NILM is robust and flexible with consistent performance on various types of appliances; including single and multiple states. It attains a test accuracy of up to 93% on the UK-Dale dataset within a single house, where a substantial number of appliances are present. In more challenging settings where electricity data is collected from different houses, IMG-NILM attains also a very good average accuracy of 85%.
