Details of Grant 

EPSRC Reference:	EP/I000755/1
Title:	Part2: Building Management linking Energy Demand, Distributed Conversion and Storage using Dynamic Modelling and a Pervasive Sensor Infrastructure
Principal Investigator:	Roskilly, Professor AP
Other Investigators:	Kray, Dr C Olivier, Professor P Ploetz, Dr T
Researcher Co-Investigators:
Project Partners:	Arup Group Ltd Building Research Establishment (BRE) Philips Siemens
Department:	NIRES Newcastle Inst for Res on Env &Sus
Organisation:	Newcastle University
Scheme:	Standard Research
Starts:	30 September 2010	Ends:	28 March 2014	Value (£):	606,679
EPSRC Research Topic Classifications:	Building Ops & Management Control Engineering Energy Efficiency Information & Knowledge Mgmt
EPSRC Industrial Sector Classifications:	Construction Energy
Related Grants:	EP/I000739/1
Panel History:	Panel Date Panel Name Outcome 17 Mar 2010 Transforming Energy Demand Through Digital Innov Announced
Summary on Grant Application Form
Commercial and residential buildings are responsible for a large proportion of carbon dioxide emissions both in the UK and globally. In 2000, 40% of the UK's total non-transport energy use was for space heating, and space heating and hot water accounted for 82% of domestic and 64% of commercial use of energy. Energy demand reduction by commercial buildings can therefore significantly contribute towards achieving the UK's broader energy consumption goals. In contrast to proposals that directly propose behaviour change interventions for the users of commercial office space, this project proposes to address a key deficit in our understanding of the quantity and nature of energy consumption in commercial settings with a view to developing novel holistic solutions including the optimisation of shared resource usage and energy storage facilities. The proposed research plans to tackle this challenge by designing and developing a sensing infrastructure that consists of networked physical (e.g. presence sensors, power consumption sensors) and virtual sensors (e.g. calendar and room booking sensors, application usage sensors) that will provide fine-grained information about how much energy is being used, for what purpose and by whom. By applying techniques from knowledge engineering, activity recognition and machine learning (e.g. Bayesian classifiers) the first stage of our approach will derive higher-level information (e.g. a meeting taking place in a particular room) and will link usage patterns (such as spikes in power consumption) to real-world activities and workflows (e.g. printing off a series of reports for a meeting). In the second stage, this information will be used to parameterise building models used in building management to more accurately predict energy usage and to optimise (decentralised) energy consumption, generation and storage. Based on these models, we will develop a decision support tool that visualises the collected data as well as the expected impact of energy saving strategies such as organisational changes and policies or the rescheduling of activities. This will enable decision makers to identify where energy is being wasted (e.g. several meeting rooms being heated despite only a few meetings being scheduled) and to formulate and evaluate strategies to reduce energy consumption. The data collected also benefits other building systems using new and emerging ISO standards for inter-operability of appliances and systems in buildings using Internet Protocols. In addition, the data will enable a better understanding of the way the building is used and how heat wasted. Through a combination of physical and virtual sensors a more accurate measurement of thermal comfort of the building's occupants will be established and thus assist in resolving ever occurring complaints and potential conflicts associated with the diverse needs for occupant comfort in buildings which also results in unnecessary overheating.
Key Findings
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Potential use in non-academic contexts
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Impacts
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Organisation Website:	http://www.ncl.ac.uk