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

6edefa12-dc31-407c-bd1a-7e804dca8c9d

Active

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

No funds listed.

Sept. 30, 2021

Sept. 30, 2025

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.

Potential Impact:
The low carbon energy systems needed to achieve the Government's carbon 2050 reduction targets promise declining generation costs, but at the price of inflexibility and intermittency. The challenge is to contain costs and improve energy system security, by building in resilience. The opportunities include: more efficient energy conversion, networks and storage technologies; improved energy control and management systems; integration of energy performance into modern methods of construction; improved measurement, display and control systems; and new business models. This will bring pervasive economic benefits: the creation of new intellectual property and expertise; businesses with the ability to compete in the huge new markets for energy efficiency and resilience, both in the UK and overseas; healthier and more productive places to work and live; and a means to address social hardship and inequalities, such as fuel poverty, which affects the health and wellbeing of society's most vulnerable. Seizing these opportunities requires leaders with multi-disciplinary knowledge, skills and whole-system perspective to break down restrictive, sector-specific silos, and drive innovation. The ERBE CDT will train such leaders.

The short and medium term impacts of the ERBE CDT will arise during the training of these leaders and through their research outputs and collaborations. These will include, but are not be restricted to: new approaches to analysis; new insights derived from large datasets; new modelling methods and ways of using existing models; new experimental techniques; field and laboratory measurement techniques; improved socio-technical methods; new manufacturing methods, devices, primary data sets, and patents; and, together with our industrial stakeholders, the integration of research into the business innovation process.

The longer term impacts will be realised over the next 40 years as ERBE graduates take on influential roles in diverse organisations, including:
- national and local governmental organisations that are developing affordable and socially acceptable evidence-based energy policies;
- energy supply and services companies that are charged with delivering a clean reliable and economical system, through deployment of energy efficiency products and technologies within an evolving energy system architecture;
- technology companies that are developing new components for energy generation and storage, new heating, cooling and ventilation systems, and smart digital controls and communications technology;
- industries that are large consumers of fuel and power and need to reduce their energy demand and curb the emission of greenhouse gases and pollutants;
- consultancies that advise on the design of energy systems, non-domestic building design and urban masterplans;
- facilities managers, especially those in large organisations such as retail giants, the NHS, and education, that are charged with reducing energy demand and operating costs to meet legally binding and organisational targets;
- standards organisations responsible for regulating the energy and buildings sectors through the creation of design guides and regulatory tools;
- NGOs and charities responsible for promoting, enabling and effecting energy demand reduction schemes;
- health and social care providers, who need to assure thermal comfort and indoor air quality, especially as our population ages and we adopt more flexible healthcare models.

The realisation of these benefits requires people with specific skills and an understanding of the associated ethical, health & safety, regulatory, legal, and social diversity and inclusion issues. Most importantly, they must have the ability to look at problems from a new perspective, to conceive, and develop new ideas, be able to navigate the RD&D pathway, and have the ability to articulate their intentions and to convince others of their worth; the ERBE CDT will develop these capabilities.