Non-urgent high energy-consuming residential appliances, such as pool pumps, may significantly affect the peak to average ratio (PAR) of energy demand in smart grids. Effective load monitoring is an important step to provide efficient demand response (DR) to PAR. In this paper, we focus on pool pump analytics and present a deep learning framework, PUMPNET, to identify the pool pump operation patterns from power consumption data. Different from conventional time-series based Non-intrusive Load Monitoring (NILM) methods, our approach transfers the time-series data into image-like (date-time matrix) data. Then a U-shaped fully convolutional neural network is developed to detect and segment the image-like data in pixel level for operation detection. Our approach identify whether pool pumps operate given thirty-minute interval aggregated active power consumption data in kilowatt-hours only. Furthermore, the PUMPNET algorithm could identify pool pump operation status with high accuracy in the low-frequency sampling scenario for thousands of household, compared to traditional NILM algorithms which process high sampling rate data and can only apply to limited number of households. Experiments on real-world data validate the promising results of the proposed PUMPNET model.

中文翻译：

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PUMPNET：一种用于泵运行检测的深度学习方法

非紧急的高能耗住宅设备（例如泳池水泵）可能会严重影响智能电网中能源需求的峰均比（PAR）。有效的负载监视是向PAR提供有效的需求响应（DR）的重要步骤。在本文中，我们专注于池泵分析，并提出了一个深度学习框架PUMPNET，以从功耗数据中识别池泵的运行模式。与传统的基于时间序列的非侵入式负载监视（NILM）方法不同，我们的方法将时间序列数据转换为类似图像（日期时间矩阵）的数据。然后，开发出一个U形全卷积神经网络，以像素级别检测和分割类图像数据，以进行操作检测。我们的方法仅在以千瓦时为单位的三十分钟间隔汇总有功功率消耗数据的情况下，确定池泵是否运行。此外，与传统的NILM算法相比，PUMPNET算法可以在数千个家庭的低频采样场景中高精度地识别池泵的运行状态，而传统的NILM算法只能处理有限采样率的数据。实际数据的实验验证了所提出的PUMPNET模型的可喜结果。