Multi-Objective Optimal Component Capacity and MPC-based Optimal Scheduling in Smart Apartment Building

In this study, we propose an optimization algorithm that achieves both optimal component capacity and high effi-ciency operation of a Smart Apartment Building (SAB). Multi-objective optimization is used to optimize component capacity, with the goal of minimizing the total cost and carbon footprint of the SAB. A Pareto front is then gener-ated to present flexibility to the SAB operator. For high efficiency operations, a Model Predictive Control (MPC)-based optimal scheduling method is used. This method can achieve higher operational efficiency than conventional methods by taking future data into account. Furthermore, it avoids over-integration of components by evaluating component capacities determined by a multi-objective optimization problem. As a result, compared to the base case, the MPC algorithm reduces operation costs by 73.0% and carbon emissions by 49.0%.