The applicability of grey-box performance-based modelling techniques in existing office buildings in the UK

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This Ph.D. investigates the applicability of grey-box performance-based modelling techniques to describe the thermal dynamics of an existing office building in the UK. The project aims to answer the question of how well grey-box models can predict the internal air temperature and heat loss of an existing office building, in order to evaluate retrofit options and control strategies. An ultra energy-efficient Passive House office building is studied as the case study to identify the best grey-box model structure in terms of providing the highest accuracy with minimum complexity and computational cost. The type, amount and quality of data coming from sensors of a Building Management System (BMS) and the level of detail (LOD) required to inform the model parameters for the office building will be explored.With the wide adoption of building automation systems (BAS) and the Internet of Things (IoT) in buildings, numerous measurements pertaining to the functioning of the buildings and their equipment are continuously gathered by sensors and other sources. This offers numerous chances to create data-driven models for building control and operation. Data-driven modelling, also known as performance-based modelling or inverse approach, is based on measured data after buildings are occupied. These models reflect the actual building thermal dynamics and provide more accurate predictions of building thermal responses. Therefore, performance-based models can be used in model-based control of space heating and cooling, fault detection of mechanical systems, retrofit evaluation for reducing operation energy consumption, shifting and shaving peak demand, and performance monitoring. Moreover, the uncertainties in Building Performance Simulation (BPS) can be reduced if the modelling process can also make use of operational measurements, recorded during building's operation. These uncertainties can result from of a wide range of dynamic, stochastic, and probabilistic elements such as building geometry, material properties, HVAC systems, occupant behaviour, appliance, use scheduling and even weather data, and increase in the case of existing buildings, where less information is available.Although machine learning techniques, such as Artificial Neural Networks (ANN) have been extensively adopted for building energy use prediction in recent decades, despite their high prediction accuracy, these techniques have some drawbacks, including a high demand for data quality, lack of interpretability and intense computational requirements. This approach also produces models with low generalisation between different buildings, which makes building-to-building comparisons of models difficult. Comparing the thermal behaviour of buildings can be of interest for the purposes of energy consumption classification. Grey-box models are a type of data-driven models that retain some physical meaning, while their parameters are calibrated using measured data. Therefore, the grey-box model is more interpretable than machine learning approach and is more computationally efficient.