A decision support model based on simulation and multi-objective optimization to determine optimum solutions for building energy performance

During decision making process for improving building energy performance, the decision maker is forced to determine the optimum solution out of many solution alternatives according to multiple criteria such as energy, cost and environment. This is a complex process in which there are three main problems. The first of these problems is the limited use of systematic decision making approaches that guide the decision maker to the most effective solution. Secondly, there is a need for knowledge support about input-output relationships in building energy performance. Thirdly, the decision maker is faced with a multi-objective optimization problem that needs to be solved. In this work, an integrated decision support model built on sensitivity analysis and simulation based multi-objective optimization is proposed to solve these problems. The proposed decision support model to determine optimum solutions for building energy performance is developed to mainly support architects as well as all the decision makers. Sensitivity analysis provides knowledge support by identifying influence levels of design parameters on building energy performance and ranking their order of importance. Multi-objective optimization, on the other hand, works powerfully on problems that require solving for multiple objectives at the same time and searching in large solution spaces. EnergyPlus building performance simulation software, Matlab, Simlab and Microsoft Excel are all used together for the execution of variance based sensitivity analysis. As for multi-objective optimization, GenOpt optimization package, EnergyPlus and Microsoft Excel are used in an integrative way. Therefore, by integrating multiple software tools towards a predefined goal and implementing automation by computer codes, a novel decision support approach is developed. The proposed model is applied to an existing school building to test its applicability. The proposed decision support model has potential for the following cases: Developing optimum solutions for building annual heating and cooling energy consumptions and Net Present Value (NPV) criteria; Identifying the primary application areas in building that should be focused on.