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

这个项目是与一个名为wifi /myma的工业合作伙伴合作的。他们的产品是一种智能插头，可以通过内部云远程控制，与亚马逊Alexa、苹果Siri和谷歌Home互操作。在他们运营的六年里，wifi插头已经卖出了3万多台，每台都能每分钟传输能量读数。包括博世（Bosch）、西门子（Siemens）、宝洁（Procter & and Gamble）和扎努西（Zanussi）在内的许多大型机构都对其产品的稳健性表示了兴趣，其中一些机构正在进行定制使用的试点测试。到目前为止，这些能源数据已经存档，该项目希望为这些数据开发一套机器学习技术管道，以实时监控电器的使用情况、健康状况和故障可能性，包括其能源读数和用途。目前，在使用机器学习进行电源管理方面的工作还很少。我们希望能够概括典型的器具行为，并测量故障/故障等重大偏差。以洗衣机为例，过载的滚筒将在旋转和冲洗中显示更高的读数，这是我们能够检测到的。首先，分类分类器使用深度递归神经网络来确定设备的类型，并在现有数据集上进行训练。能量读数有些复杂；最明显的是器具种类的严重不平衡。例如，与洗衣机相比，连接智能插头的洗碗机要少得多。我们必须在模型训练中评估和应用数据科学/分析技术来弥补这一点。此外，作为原始简介的一部分，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时间空闲状态并关闭设备以节省能源。工业合作伙伴目前正在开发一种增强版的智能插头，能够每秒捕获50个能量读数，而不是每分钟1个。这应该提供比现有档案更精确的行为分析，但我们必须研究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，wifi拥有庞大的能源数据档案，并有办法继续以更丰富的细节生产这些数据，但目前还没有办法为他们的客户利用这些数据。该项目旨在开发一个全面的智能管道，以获得对电器的新深度理解，从故障检测到节能。

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in the robustness of their product and some are in the process of pilot testing for bespoke use.This energy data has been archived until now, and the project looks to develop a pipeline of machine learning technologies for this data to monitor the usage, health and likelihood of failure of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.