Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of
smart cities. Whether it is healthcare monitoring, building automation, energy management, or traffic
management, use cases of capability enhancement using machine learning have been significant in
recent years. This paper aims to propose a modeling approach for scheduling energy consumption
within smart homes, based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy
management plays a significant role in reducing energy consumption and carbon emissions as com-
pared to centralized energy generation. Multiple energy consumers can schedule energy-consuming
household tasks using home energy management systems in coordination to reduce economic
costs and greenhouse gas emissions. In this work, such a home energy management system is used
to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption
tasks based on a multi-objective optimization method that considers environmental and economic sustainability. There are two conflicting objectives: minimizing daily energy costs and reducing carbon
dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which
electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and
electricity consumption are scheduled. An implementation of the proposed model is undertaken in a
model smart building consisting of thirty homes under three pricing schemes. A Pareto curve illustrates the trade-off between cost and CO2 emissions