Development of a Cognitive Digital Twin for Building Management and Operations

Abstract

Cognitive Digital Twins (CDTs) are defined as capable of achieving some elements of cognition, notably memory (encoding and retrieval), perception (creating useful data representations), and reasoning (outlier and event detection). This paper presents the development of a CDT, populated by construction information, facility management data, and data streamed from the Building Automation System (BAS). Advanced machine learning was enabled by access to both real-time and historical data coupled with scalable cloud-based computational resources. Streaming data to the cloud has been implemented in existing architectures; to address security concerns from exposing building equipment to undesirable access, a secure streaming architecture from BACnet equipment to our research cloud is presented. Real-time data is uploaded to a high-performance scalable time-series database, while the ontology is stored on a relational database. Both data sources are integrated with Building Information Models (BIM) to aggregate, explore, and visualize information on demand. This paper presents a case study of a Digital Twin (DT) of an academic building where various capabilities of CDTs are demonstrated through a series of proof-of-concept examples. Drawing from our experience enhancing this implementation with elements of cognition, we present a development framework and reference architecture to guide future whole-building CDT research.