Appliance Disaggregation and Optimal Scheduling

伦敦海德鲁（LH）是一家本地分销公司，为安大略伦敦地区的15万多名消费者提供服务。LH ** 目前正在实施一项实时能源管理试点，为每个参与家庭提供一个智能能源 ** 中心、智能插头和一个远程启动多达五个电器的应用程序。通过该试点，LH将评估 ** 临界峰值定价（CPP）对电力消耗模式的影响，通过移动的应用平台提供的上下文信息 ** 与消费者互动的能力，以及连接到智能插头的本地电器 ** 运行时间表的自动化过程。这一努力在激励和促进消费者经济和可持续地使用 ** 电力方面具有重要意义。拟议项目由两个主要部分组成。首先，机器学习将被应用于分解智能电表和插头生成的电力使用数据，以便在一天中的不同时间间隔内识别消费者家庭中的活动设备。建议的分类器将使用在LH的最先进的研究 ** 设施中生成的高粒度数据进行训练。消费者将能够通过他们的移动的 ** 应用程序提供关于当地分类准确性的反馈，然后用于为每个家庭定制和微调分类器。在第二阶段，利用设备分解的最佳 ** 调度将被集成到智能能源中心，以便根据经济可行性，可持续性和消费者偏好自动化本地设备的操作 ** 时间表。

London Hydro (LH) is a Local Distribution Company serving more than 150 000 consumers in the region of London, Ontario. LH is**currently implementing a real-time energy management pilot that makes available to each participating household a smart energy**hub, smart plugs and an application to remotely actuate up to five appliances. Through this pilot, LH will assess the impact of**critical peak pricing (CPP) on power consumption patterns, the ability to engage with consumers through contextual information**provided through the mobile application platform and the automation process of the operation schedules of local appliances**connected to smart plugs. This effort has significant implications in motivating and enabling economical and sustainable usage of**electricity by consumers.**The proposed project is composed of two main components. First, machine learning will be applied to disaggregate the power**usage data generated by smart meters and plugs in order to identify active appliances in a consumer's household during various**intervals over a day. The proposed classifier will be trained using highly granular data generated in the state-of-the-art research**facility at LH. Consumers will be able to provide feedback regarding the accuracy of local disaggregation through their mobile**application which is then used to customize and fine-tune the classifier for each household. In the second phase, optimal**scheduling that leverages on appliance disaggregation will be integrated into smart energy hubs for automating the operation**schedule of local appliances based on economical viability, sustainability and consumer preferences.