A deep learning empowered framework for enabling energy savings via non-intrusive load monitoring

该项目是与一个名为WifiPlug/Myma的工业合作伙伴合作的。他们的产品是智能插头，可以通过内部云进行远程控制，该云可以与Amazon Alexa、Apple Siri和Google Home互操作。在他们六年的运营中，WifiPlug已经售出了超过3万台，每台设备每分钟都会传输它的能量读数。包括博世、西门子、宝洁和Zanussi在内的许多大型组织都表达了对其产品的健壮性的兴趣，一些组织正在为定制使用进行试点测试。到目前为止，这些能源数据一直被存档，该项目希望为这些数据开发一个机器学习技术管道，以根据能源读数和用途实时监控电器的使用情况、健康状况和故障可能性。目前，在利用机器学习进行电源管理方面所做的工作还很少。我们希望能够概括出典型的电器行为，并将显著的偏差作为故障/故障进行测量。以洗衣机为例，过载的滚筒在旋转和漂洗时会显示较高的读数，我们将能够检测到这一点。首先，分类分类器使用深度递归神经网络在打开时确定电器的类型，并在现有数据集上进行训练。能源读数有一些复杂之处；最明显的是电器类型严重失衡。例如，连接到智能插头上的洗碗机比洗衣机少得多。我们必须在模型培训中评估和应用数据科学/分析技术来弥补这一点。此外，作为最初简报的一部分，可以在边缘监控设备的使用习惯。使用预测分析，我们希望能够确定设备何时进入长期空闲状态并关闭设备电源以节省能源。工业合作伙伴目前正在开发智能插头的增强版本，能够每秒捕获50个能源读数，而不是每分钟1个。这应该会比现有的档案在行为分析方面提供更高的精度，但我们必须调查存档的读数是否以及如何在新硬件的机器学习管道中得到最好的利用。总而言之，Wifilug拥有大量的能源数据档案和继续生产更丰富细节的方法，但目前无法为客户利用这些数据。该项目旨在开发一种全面、智能的管道，以获得对从故障检测到节能的家用电器的新的深入了解。

This project is in conjunction with an industrial partner named Wifiplug/myma. Their product, a smart plug, can be controlled remotely via an in-house cloud which is interoperable with Amazon Alexa, Apple Siri and Google Home. In their six years of operation, Wifiplug have sold over 30,000 units, and each one transmits its energy readings each minute. Numerous large organisations, including Bosch, Siemens, Procter & and Gamble and Zanussi have expressed interest in the robustness of their product and some are in the process of pilot testing for bespoke use.This energy data has been archived until now, and the project looks to develop a pipeline of machine learning technologies for this data to monitor the usage, health and likelihood of failure of an electrical appliance from its energy readings and purpose in real time. Presently, little work has been done in the field of power management using machine learning.We hope to be able to generalise typical appliance behaviour and measure significant deviations as malfunctions/failures. Using laundry machines as an example, an overloaded drum will exhibit higher readings in the spin and rinses, which we will be able to detect. This begins with a categorical classifier using a deep recurrent neural network to determine the type of appliance when it is switched on, trained on the existing dataset. There are some complications with the energy readings; most notably a heavy imbalance in types of appliance. For example, there are significantly less dishwashers connected to smart plugs than laundry machines. We must assess and apply data science/analysis techniques to compensate for this in model training. Furthermore, as part of the original brief, usage habits of the appliance can be monitored on edge. Using predictive analytics, we hope to be able to determine when an appliance will enter a prolonged state of idleness and power down the appliance to conserve energy.The industrial partner is currently developing an enhanced version of the smart plug, capable of capturing 50 energy readings per second as opposed to 1 per minute. This should offer greater precision in behaviour analysis than the existing archives, but we must investigate whether and how the archived readings can be best utilised in the machine learning pipeline for the new hardware.To summarise, Wifiplug have vast archives of energy data and the means to continue producing it in far richer detail, yet currently have no way of leveraging it for their customers. This project looks to develop a comprehensive, intelligent pipeline to gain a new depth of understanding in appliances, from failure detection to energy conservation.