LAUSR

Smart grids offer better energy management for consumers as well as energy companies using bi-directional communication and control. With the advent of smart homes, energy companies can balance energy supply and demand to a large extent using many sensors/meters deployed. They can also nudge consumers to shift their demands to off-peak hours for load balancing and monetary benefits. We propose a decentralized demand scheduling algorithm that minimizes consumer discomfort and electricity cost of a household. Our algorithm utilizes only aggregated energy consumption of a household to derive optimal appliance level demand schedules. Furthermore, a low-complexity energy disaggregation algorithm is proposed to derive fine-grained appliance information and consumer preferences. We propose three important coefficients related to the energy usage of consumers. We utilize them to derive optimal day-ahead demand schedules. The decentralized algorithm is empirically evaluated using real-world energy usage data from open datasets, which include our own deployment. Our proposed scheduling algorithm saves up to 30% energy cost. This paper is one of the first to derive day-ahead schedules using real-world data from multiple households.