A framework for knowledge representation learning-based building control - Mugumya, Kevin Luwemba (2025)
Current Building Automation Systems (BASs) have crucial context-awareness limitations that must be addressed before they can reach human-like levels and better adapt to the dynamic needs of modern buildings. Among other limitations, our buildings still lack sensors, actuators, and control agents that can learn reliable models of the environment and plan complex action sequences. Moreover, modern Machine Learning (ML)-backed BASs, though trained on massive datasets, are usually overly specialised (trained for one task) and brittle (prone to errors). In contrast, human learning is very efficient, and with only a few examples, we can find intuitive ways to complete a task while generalising our knowledge to other tasks. To address the above limitations, this thesis proposes a foundational framework that aims to advance the context-awareness capabilities of BASs using knowledge graphs and Knowledge Representation Learning (KRL). At the framework’s core is the notion of using Semantic Web Technologies (SWT) to model the semantic relationships between different building components. These relationships are then packaged inside a network-like data structure called a Building Information Modeling (BIM)-based Knowledge Graph (BIM-KG), and KRL is applied to learn the hidden patterns within the BIM-KG. During the learning phase, KRL utilises message-passing to propagate the learnt information throughout all nodes/entities in the BIM-KG. This research hypothesises that building automation agents can leverage this notion of message-passing to aggregate contextual information from all entities in the graph and use it to continuously update their understanding of a building’s systems and components. The perception is that imbuing building automation agents with holistic information about the buildings they control can presumably support context-aware decision-making during downstream automation tasks.