Data-Smart Building Case Studies

Lead Research Organisation: University College London
Department Name: Bartlett Sch of Env, Energy & Resources


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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Description The main objective of the Data-Smart Buildings (DSB) project is to develop knowledge, industry processes and digital technology tools that address some of the challenges identified above to support delivery of improved energy management and building systems control in new and extant non-residential buildings. To achieve this, and to address the challenges identified above, the specific objectives of the proposed work are as follows:
1. Identify requirements and develop a process for designing and delivering improved control.
2. Develop approaches for designing building-specific predictive controllers for use in both new and existing buildings
3. Develop an open, standards-based, semantic data model that captures the minimum required information on the domain of buildings and their building services to support the improved control methods to be developed.
4. Perform field testing in representative UK buildings and disseminate successful test results as case studies.
5. Apply and evaluate the use of existing Centre for Research in Energy Demand Solutions (CREDS) modelling tools to assess the potential policy, regulatory, and business pathways in relation to the use of Building Energy Management Systems (BEMS) for end-use demand reduction.
6. Ensure fitness for purpose, through the formation of an Advisory Panel to scrutinise the proposed processes for delivering improved energy efficiency through improved control as well as the technical methods and tools to implement these processes.
Exploitation Route The focus of the research is the effective control of indoor environments in non-residential (non-domestic) buildings. The main beneficiaries will be:
• Owners and operators of buildings, who will be able to control the environmental conditions in their buildings more effectively, provide better quality indoor environments and save energy in the operation of building services.
• Building occupants and tenants, who will have better controlled indoor environments, avoiding over-heating and under-heating, receiving adequate levels of ventilation and appropriate levels of lighting without disabling glare, and being more comfortable and productive as a result.
• Controls equipment suppliers and system integrators, who will be able to offer their customers improved software for the effective operation of controls in new and existing buildings. This will serve to optimise the operation of buildings services, identify faults and allow buildings to interact with the grid to facilitate demand management and demand response.
• Building services engineers and building services contractors, who will have reduced design and installation costs and fewer call-backs.
• Energy consultants and Energy Services Companies (ESCOs), who will share in the income to be derived from the ability to respond to dynamic electricity pricing.
• Spin-offs to other sectors from the Model Predictive Control (MPC) development
Sectors Digital/Communication/Information Technologies (including Software),Education,Energy,Government, Democracy and Justice,Other