Machine learning for accurate estimation of electrical device usage from smart meters data

Smart meters give us valuable insight into how electricity is used. However, the value is greatly increased if the information can be “disaggregated” into the consumption by each device or activity. This information is of value to customers, retailers, distribution companies and market operators. Providing customers with a timely disaggregation information helps them alter their behaviour to reduce their total energy consumption. Retailers and customers benefit from reduced “bill shock” when customers are told of reasons for consumption spikes. Accurate disaggregation information will help the retailers designing new tariffs, such as time-of-use tariffs and dynamic pricing. Another aim of energy disaggregation is to find trends in electricity usage. This information will support decision making in distribution companies by providing a better understanding of customers patterns, and in market operators by providing more accurate demand forecasts.
A major challenge in disaggregation is the scarcity of labelled data. Sub-metering all devices is a straightforward yet expensive solution for collecting labels, and visual inspection by domain experts is time consuming and hence expensive. In this research project, we develop and deploy artificial intelligence algorithms for disaggregation of smart meter data using unlabelled or sparsely labelled data. The algorithm combines both domain expert knowledge and machine learning for continuous learning and incremental adaptation in order to gradually decrease the amount of manual intervention per house and also increase the overall accuracy and efficiency of the disaggregation.