Smart grids offer better energy management at consumer premises as well as energy companies side using bi- directional communication and control. Energy companies can balance energy supply and demand to a large extent, with the advent of smart homes. 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 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 work is one of the first to derive day-ahead schedules using real-world data from multiple households.