A Digital Twin framework for multi-objective optimization

This thesis represents the culmination of the Msc civil engineering course at the University of Agder. This thesis aims to attempt to define a framework for implementing digital twins in an investment cost/energy consumption optimization process. The methodology applied is a complex software hierarchy. The original dataset rests on randomly generated values of thermal transmittance, which are analysed in IDA ICE simulations, and compared to existing materials identified in the Norsk Prisbok for cost estimation. The results are optimized using a combination of Artificial Neural Networks and a multi-objective optimization algorithm, the elitist non-dominated sorting algorithm NSGA-II. The research question this thesis attempts to answer is: How can digital twins be implemented to reduce energy-consumption and costs in buildings? This thesis concludes that “A digital twin may be implemented to translate energy consumption and cost-optimization into an easily interpreted result that serves as a foundation for efficient decision-making.” This conclusion is based on the functionality of the various steps in the framework: Accuracy of ANN models, NSGA-II performance and visual presentation. The thesis presents a functional framework with a high degree of automation. Furthermore, applying said framework to a case study identified a potential energy consumption reduction of 35 % and a reduction in investment costs by 5 %