Optimizing Building’s Energy Efficiency Analysis Using Multi-Objective Particle Swarm Optimization (MOPSO)
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We successfully built a model and application that was constructed using the Revit API to help the building designers conduct the energy analysis by modifying the material of the elements, like walls, windows, roofs, and others. This work has addressed the necessity of computerized design work for energy buildings, which was too time-consuming and complex. With the built-in Revit API, the building designers can quickly calculate the energy usage in a building as early as possible from the design stage. However, there was a drawback in our previous work: the building designers selected parameters of materials using intuition and manual processing. In this study, we enhance the capability of previous work with intelligent automation to do selection, analysis, and optimization based on several factors, like the low value of Overall Thermal Transfer Value (OTTV) and Roof Thermal Transfer Value (RTTV) and the cost of materials. These three factors are defined as the multi-objective functions of this study. We identified eight dimensions generated from the list of materials used in a building, which led us to exploit particle swarm optimization (PSO) to find the optimum solutions for the energy analysis. Here, we use the built model to generate parameters and a list of materials from the library, and then we identify six items for OTTV and two items for RTTV as dimensions to run the multi-objective particle swarm optimization (MOPSO). The solutions are presented using the Pareto Front, allowing the building designer to focus on the set of efficient choices, including the tradeoffs within the solutions. We compared the performance of the enhanced approach with previous work, yielding major improvements of 52.43% for OTTV and 44.51% for RTTV, resulting in a 23.82% improvement.
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