Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit

A method of fast multi-objective optimization and decision-making support for building retrofit planning is developed, and lifecycle cost analysis method taking into account of future climate condition is used in evaluating the retrofit performance. In order to resolve the optimization problem in a fast manner with recourse to non-dominate sorting differential evolution algorithm, the simplified hourly dynamic simulation modeling tool SimBldPy is used as the simulator for objective function evaluation. Moreover, the generated non-dominated solutions are treated and rendered by a layered scheme using agglomerative hierarchical clustering technique to make it more intuitive and sense making during the decision-making process as well as to be better presented. The suggested optimization method is implemented to the retrofit planning of a campus building in UPenn with various energy conservation measures (ECM) and costs, and more than one thousand Pareto fronts are obtained and being analyzed according to the proposed decision-making framework. Twenty ECM combinations are eventually selected from all generated Pareto fronts. It is manifested that the developed decision-making support scheme shows robustness in dealing with retrofit optimization problem and is able to provide support for brainstorming and enumerating various possibilities during the decision-making process.