The CRE Journal article "Integration of building science and data science to de-risk an affordable strategy for building decarbonization" demonstrates the tools and the processes necessary to affordably decarbonize new and existing buildings.
Building science, in isolation, delivers high-performance buildings at one point in time. Data science, in isolation, tracks a building's performance against itself over time. The merging of building science and data science achieves and maintains high-performing buildings over the life of each building.
To merge building science and data science, we must standardize the real-time, time-series, independent data layer (IDL) and extract data from operational technologies in the most cost-effective, scalable and reliable manner possible. The physics-based sustainability model uses the time-series data from operational technologies for calibration. Once the model is operationalized, the IDL manages the dynamic time-series data from the model to inform decarbonization master planning, monitoring-based commissioning, interrogation-based commissioning and testing of advanced data analytics prior to deployment.
By merging building science and data science, building owners will know immediately if their building is performance against expectations. This is how we significantly reduce the financial and performance risks of investing in decarbonization.
Integration of Building Science and Data Science to De-risk an Affordable Strategy for Building Decarbonization