Appliance Disaggregation and Optimal Scheduling

点击翻译按钮获取中文摘要

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.