Multi-Objective Optimization based Energy and Comfort Management in Smart Buildings
Mentor 1
Lingfeng Wang
Location
Union Wisconsin Room
Start Date
29-4-2016 1:30 PM
End Date
29-4-2016 3:30 PM
Description
Smart building technologies are becoming more popular in the next-generation’s commercial and residential buildings where intelligent control methods are implemented. Balancing the energy consumption and the residents’ comfort is the primary challenge in smart building control, so we hope to minimize the conflicts between the users’ comfort and the total energy consumption. In this research, a multi-agent based control framework is proposed to manage the energy consumption and occupants’ comfort effectively. Since the comfort level and energy consumption are two major control objectives in the energy management system, we propose to use two multi-objective optimization methods for obtaining the Pareto-optimal solutions. The first method is multi-objective particle swarm optimization (MOPSO), and the other method is weighted aggregation approach. The MOPSO generates a set of trade-off solutions, from which users can select a specific one based on their preferences/needs. Weighted aggregation approach is introduced for solving conflicts in multi-objective problems by multiplying each objective with a user-defined weight. Multi-objective optimization is effective for energy and comfort management in building automation since trade-off solutions are useful and meaningful for balancing different conflicting designs or control objectives in the complex building energy and comfort management systems. What’s more, the methods we use indeed help with decision-making in energy consumption and occupants’ comfort management in complex building environments, which make it possible for smart buildings to stand out and lead to an environmental friendly living standard.
Multi-Objective Optimization based Energy and Comfort Management in Smart Buildings
Union Wisconsin Room
Smart building technologies are becoming more popular in the next-generation’s commercial and residential buildings where intelligent control methods are implemented. Balancing the energy consumption and the residents’ comfort is the primary challenge in smart building control, so we hope to minimize the conflicts between the users’ comfort and the total energy consumption. In this research, a multi-agent based control framework is proposed to manage the energy consumption and occupants’ comfort effectively. Since the comfort level and energy consumption are two major control objectives in the energy management system, we propose to use two multi-objective optimization methods for obtaining the Pareto-optimal solutions. The first method is multi-objective particle swarm optimization (MOPSO), and the other method is weighted aggregation approach. The MOPSO generates a set of trade-off solutions, from which users can select a specific one based on their preferences/needs. Weighted aggregation approach is introduced for solving conflicts in multi-objective problems by multiplying each objective with a user-defined weight. Multi-objective optimization is effective for energy and comfort management in building automation since trade-off solutions are useful and meaningful for balancing different conflicting designs or control objectives in the complex building energy and comfort management systems. What’s more, the methods we use indeed help with decision-making in energy consumption and occupants’ comfort management in complex building environments, which make it possible for smart buildings to stand out and lead to an environmental friendly living standard.
