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 d...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.展开更多
文摘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.
