Multi-objective optimization for decision-making of energy and comfort management in building automation and control

Smart buildings are becoming a trend of next-generation's commercial buildings, which facilitate intelligent control of the building to fulfill occupants’ needs. The primary challenge in building control is that the energy consumption and the comfort level in a building environment often conflict with each other. In this study, to effectively manage the energy consumption and occupants’ comfort, a multi-agent based control framework is proposed for smart building applications. The energy consumption and the overall comfort level are considered as two control objectives in the system design. Two multi-objective optimization methods including multi-objective particle swarm optimization (MOPSO) and weighted aggregation are utilized to generate the Pareto fronts which are made up of Pareto-optimal solutions. These tradeoff solutions are useful to informed decision-making for energy and comfort management in the complex building environments.

► A multi-agent control architecture is developed for building energy management. ► Occupants’ comfort level and total energy consumption are treated as two conflicting control objectives. ► An extended particle swarm optimization is used to deal with the multi-objective optimization problem.