This paper aims to document the systems implemented and share insights gained during the process of achieving Building Energy Model (BEM) and Building Automation System (BAS), interoperability for effective control and management of real-world built environments. The target building is a net-positive energy building, where all sensor and operational data is integrated into a centralized data repository, otherwise known as a data lake. This data serves as the input to a BEM that can not only model building performance, but also interface with the BAS system to optimize building operation on a system level scale. However, the ability to collect, store, and query data alone is insufficient to establish a successful system, as poor data quality can lead to inaccurate model output and ineffective building control. While there exist documented efforts and standards, there is a lack of literature addressing post-construction data system management. Yet we have consistently found that data acquisition must be complemented by robust measures to ensure data usability, which includes actively tracking sensor performance, data quality, and key building performance metrics to ensure the integrity of the collected data. This paper outlines a data validation framework that uses equipment and correlation groupings to enable the usage of both physics-based and data-driven models. This hybrid approach makes it possible to not only perform sensor data validation and reconstruction, but also monitor equipment performance and sensor degradation. By employing these strategies and tools, we aim to enhance the feasibility and effectiveness of BEM and BAS interoperability, ultimately improving building control and management in real-world applications.
| File Size : | 1
file
, 4.7 MB |
| Note : | This product is unavailable in Russia, Belarus |
| Number of Pages : | 9 |
| Product Code(s) : | D-CH-24-C072 |
| Published : | 2024 |
