A novel hybrid agent-based model predictive control for advanced building energy systems

The development of new energy efficient components and complex energy concepts in recent years has heightened the need to design advanced control strategies. The main objective of this research is to develop a control strategy for building energy systems to save primary energy by applying the concept of multi-agent systems. Most of the advanced control strategies have been developed to be energy efficient, while their objectives are obtained from energy analysis. However, an energy analysis is unable to provide information on the quality of energy streams flowing through a system. In this study, exergy is selected as the objective of the optimization. To reach the goal of this research, an agent-based control for building energy systems using the exergy cost functions is developed. Agent-based control, which takes into account the interactions among the components of the system, offers a promising solution to the need for more advanced control strategies for complex building energy systems. The classical agent-based control developed in this study is combined with model predictive control, which leads to a novel hybrid agent-based model predictive control for the optimization of advanced building energy systems from an exergy point of view. For evaluation purposes, a case study is defined and modeled, which is controlled by a reference control and the agent-based under the same circumstances through software-in-the-loop simulations. The results show that the agent-based control is able to reduce the primary energy consumption by 2 percent while maintaining the room air temperature at the same level of the reference case.