From Physics to AI: Modelling Buildings' Thermal Dynamics
Creators: name: Morkūnaitė, Lina
name: Rasheed, Adil
name: Pupeikis, Darius
Publisher: name: Zenodo
Year: 2024

description: In an era marked by increasing environmental concerns and the pressing need for energy independence, the role of AI in AEC industry has taken the centre stage. Heating energy can make up to 70% of building energy use. Therefore, modelling buildings’ thermal dynamics and predicting energy consumption is essential for heating energy optimization. It also increases the elasticity of the power market by allowing the combined heating systems balancing. With the advent of AI in AEC industry the adaptation of deep learning models substantially grew, however, the best modelling approach highly depends on the complexity of an asset and available data.

This work provides practical recommendations while choosing the best modelling approach by analysing the three overarching paradigms of physics-based white-box models, data-driven black-box models, and hybrid grey-box models in terms of model accuracy, computational efficiency, generalizability, interpretability and robustness. A matrix mapping the relation between the asset and modelling complexity is presented and discussed. Further, the data resolution issues are introduced, and recommendations are provided for dealing with data in a highly controlled environment.

Computational models based on the underlying physics have been used long since to approximate the building’s thermal dynamics. However, physics-based models requires detailed knowledge not only of the physics themselves but also of the building’s properties which are often based on approximations by standards.

In the last decade, decreases in sensor prices along with the development towards IoT enabled the collection of extensive building data, driving the adoption of data-driven models, particularly AI-based ones. In contrast to physics-based models, they do not require knowledge about the building properties. Furthermore, they can account for levels of detail in the model that a physics-based approximation cannot achieve. However, they require large amounts of data and are prone to poor performance outside of the state space covered by the data. Furthermore, their black-box nature makes them difficult to trust and impossible to interpret.

Recently, combinations of physics-based white-box models and data-driven black-box methods have been introduced under the name of hybrid and grey-box modelling. These hybrid models can potentially combine the best of both physics-based and data-driven models, which means preserving the reliability of physics-based models while they - just like data-driven models – do not require building-specific knowledge. However, in specific user cases they can result in subpar performance.

The choice of the best approach among the three depends on the specific application and available information. The authors compare the strengths and weaknesses of these approaches and offer practical recommendations to determine the most suitable method for individual user cases. Key considerations include the availability and detail of building information, the required level of detail in the model, and the quality, variety, and completeness of historical data.