Previous research has primarily focused on achieving net-zero energy use during the operational phase of buildings. However, recent studies indicate that such buildings do not always reduce total energy use and, in some cases, may even increase it from a life cycle perspective due to the greater demand for materials and components. Therefore, it is essential to analyze energy use over the entire buildings’ life cycle.

In this project, we aim to explore how simulation and optimization methods, such as genetic algorithms, AI, and machine learning, enable to realize net-zero energy balance for buildings from a life cycle perspective. We are developing and testing tools to analyze various measures and applying them to multiple case studies to gain deeper knowledge in this area.

The primary target groups for this project are designers, construction companies, and property owners. They can use the developed tools and new insights to identify optimal measures and make sustainable decisions that ensure net-zero energy balance from a life cycle perspective in their projects.

Policymakers can also benefit from these results to revise building regulations, thereby contributing to the goal of a decarbonized building stock.

We hope that the developed tools, along with the new knowledge gained in this project, will contribute to achieving a decarbonized building stock and a sustainable built environment.