Machine learning and price-based load scheduling for an optimal IoT control in the smart and frugal home

Energy and AI

Clara Schaye¹  Rachneet Kaur²  Daniel C. Yee³  Kevin Thompson⁴  Rachel Zilz⁴  Richard B. Sowers⁴  R.S. Sreenivas⁴ 

[1] Department of Industrial and Enterprise Systems Engineering, University of Illinois, Urbana, IL 61801, USA;Corresponding author.;Department of Electrical and Computer Engineering, University of Illinois, Urbana, IL 61801, USA;Department of Industrial and Enterprise Systems Engineering, University of Illinois, Urbana, IL 61801, USA;

关键词: Power markets;    Load scheduling;    Machine learning;    Feature analysis;    Internet of Things;    Autonomous scheduling;   

DOI  :  

来源: DOAJ

【 摘 要 】

We pose and study a scheduling problem for an electric load to develop an Internet of Things (IoT) control system for power appliances, which takes advantage of real-time dynamic energy pricing. Using historical pricing data from a large U.S. power supplier, we study and compare several dynamic scheduling policies, which can be implemented in a smart home to activate a major appliance (dishwasher, washing machine, clothes dryer) at an optimal time of the day, to minimize electricity costs. We formulate our scheduling task as a supervised machine learning classification problem which activates the load during one of two preferred time bins. The features used in the machine learning problem are hourly market, spot and day-ahead prices along with delayed label of the prior day. We find that boosting tree-based algorithms outperform any other classification approach with measurable reduction of energy costs over certain types of naive and static policies. We observe that the delayed label has most predictive power across features, followed, on average, by spot, hourly market, and day-ahead energy prices. We further discuss implementation issues using a micro controller system coupled with cloud-based serverless computing and dynamic data storage. Our test system includes an interactive voice interface via an intelligent personal assistant.

【 授权许可】

Unknown