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Details of Grant 

EPSRC Reference: EP/V011936/1
Title: Data-Smart Building Case Studies
Principal Investigator: Ruyssevelt, Professor PA
Other Investigators:
Rovas, Dr D
Researcher Co-Investigators:
Project Partners:
Department: Bartlett Sch of Env, Energy & Resources
Organisation: UCL
Scheme: Standard Research - NR1
Starts: 01 June 2020 Ends: 31 August 2022 Value (£): 202,117
EPSRC Research Topic Classifications:
Energy Efficiency
EPSRC Industrial Sector Classifications:
Energy Construction
Related Grants:
Panel History:  
Summary on Grant Application Form
The building sector accounts for about 40% of total final energy use and harbours the enormous potential to save energy and reduce CO2-emissions in a cost-effective way. Many of these cost-effective opportunities do not require significant capital outlay, relying instead on sound decision making and proficient implementation of building maintenance and operational control strategies. Indeed, many buildings are poorly commissioned from inception such that they never operate as designed. Even for well-commissioned buildings, the performance of systems might degrade over time, and this can go unnoticed resulting in poor performance. The extent of the opportunity is highlighted by Katipamula and Brambley (2005) who claim that "poorly maintained, degraded, and improperly controlled equipment wastes an estimated 15% to 30% of the energy used in commercial buildings."

The importance of these issues in now widely acknowledged. For example, within the Mission Innovation Challenge 7 on Affordable Heating and Cooling a specific task on Predictive Maintenance and control Optimization (PMO) is foreseen. A key pre-requisite to achieve progress in such a task is access to high-quality contextual and metered data. In the UK, the Smart Meter Research Portal project seeks to create and make publicly available such a resource by linking contextual to smart meter data. Rich data sets are becoming increasingly available, but the question remains on how to maximise the insights and actionable information that can be extracted from these data.

The work planned for this project seeks to explore the ways in which such datasets can be beneficially used to address the issue of PMO; in addition, it will seek to explore the benefits of more integrated building-level integration. The main premise is that contextual information together with highly granular data can be processed to identify performance degradations and inform predictive maintenance decisions. It then makes sense to seek to tune and optimise operation by more intelligent control strategies. Beyond technical challenges, for effective adoption of such new data-centric approaches, the value proposition needs to be identified for various stakeholders, and the identification of potentially viable business models, regulatory boundaries and procurement mechanisms. Around the world, good practice examples and evidence are appearing related to the benefits and practical application pathways towards Data-Smart Buildings.

The scope of this work is potentially vast and cannot be addressed within a single project. The recently established IEA EBC Annex 81, Data-Driven Smart Buildings seeks to pool resources from around the world to create a critical mass of researchers then can address such a challenge. This project will support the UK's involvement and leadership on Subtask D of the Annex, on Case Studies and business models.

The case studies to be collected will consider the logical steps on the journey from measurement (gathering data) to building management (actionable knowledge and building control). These aspects are to include latest research developments that span from: (i) data collection and data-modelling; (ii) data-driven modelling; (iii) capturing expert knowledge but also using Artificial Intelligence, to create data-driven applications and services, to; (iv) the utilisation and adoption of new services and business models. This approach follows the adage that 'you can't manage what you don't measure'. To ensure that the evidence is relevant, the work will cover a range of representative building typologies, climates and occupant applications. The findings will be communicated to stakeholders.

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