The Energy Performance of Building Directive obligated all European countries to reduce the energy requirements of buildings while simultaneously improving indoor environment quality. Any such improvements not only enhance the health of the occupants and their productivity, but also provide further economic benefits at the national level. Accomplishing this task requires a method that allows building professionals to resolve conflicts between visual and thermal comfort, energy demands, and life-cycle costs. To overcome these conflicts, this study exploits the incorporation of building information modelling (BIM), the design of experiments as an optimization algorithm, and the analytical hierarchy process (AHP) into a multi-criteria decision-making method. Any such incorporation can (i) create constructive communication between building professionals, such as architects, engineers, and energy experts; (ii) allow the analysis of the performance of multiple construction solutions with respect to visual and thermal comfort, energy demand, and life-cycle costs; and (iii) help to select a trade-off solution, thereby making a suitable decision. Three types of energy-efficient windows, and five types of ground floors, roofs, and external wall constructions were considered as optimization variables. The incorporation of several methods allowed the analysis of the performance of 375 construction solutions based on a combination of optimization variables, and helped to select a trade-off solution. The results showed the strength of incorporation for analyzing big-data through the intelligent use of BIM and a simulation in the field of the built environment, energy, and costs. However, when applying AHP, the results are strongly contingent on pairwise comparisons.
ML Guided Energy-performance Trade-off Estimation for Uncore Frequency Scaling
