Energy Research
Overview
You may be wondering where all the energy data inputted into SMEasure goes and how it is of value to building energy management research.
First, lets set the scene: the UK built environment uses a huge amount of energy and is responsible for about 45% of the UK’s greenhouse gas emissions (15% of that is commercial buildings and 30% from homes). We need to reduce energy use in buildings if we are to reach our national aim of reducing greenhouse gas emissions by 80% by 2050, and generally be more energy secure.
To effectively and efficiently reduce emissions from buildings, we must understand:
The problem is, we know surprisingly little about energy use in buildings. At the moment the commercial building stock is characterised through extrapolation from small sample sizes, static pictures of energy use rather than an understanding of energy use over time, and an over-reliance on theoretical rather than actual energy use.
So, SMEasure’s approach of collecting real energy use, for a diverse array of building types, over time and in relation to degree day data, really plugs a hole in our knowledge base.
The data collected through SMEasure will be used by researchers in the Lower Carbon Futures (LCF) group, part of the highly respected Environmental Change Institute (ECI) at Oxford University. LCF has twenty years of experience doing interdisciplinary approach to energy research, looking at both the materials and technologies related to building energy (use and generation), as well understanding how these interact with the practices and perceptions of a building’s occupants. Research insights from LCF are fed back to industry as well as to government in the form of policy recommendations.
Science & modelling
SMEasure will generate a lot of anonymous energy use data across a range of building types and over time. Below lists a number of particularly pressing research questions that SMEasure data will help to answer:
1) How can real-world energy data improve how buildings are characterised in stock-models?
Stock modelling involves modelling whole building stocks over time. The Lower Carbon Futures group has developed both domestic and non-domestic building stock models which help us to think about what sort of changes might be required if emissions from all buildings are to be reduced by 80% compared to 1990 (the reduction target of the UK Climate Change Act).
The organisations and businesses using SMEasure are helping us build up a dataset of real-time energy use across many building types. In the future we can use this to make sure we have characterised buildings correctly in out models.
The more sophisticated models come from better describing buildings mathematically and this means that questions you ask of models can be more accurately answered. In turn the quality of information used by those making investment decisions and policy is higher. The kinds of questions that can be explored using a stock model are:
Good quality models mean better informed decisions and much more realistic expectations about what policy and investment can deliver.
2) How do performance curves vary across building types?
The performance curve is a graph showing you how energy use changes as the temperature changes outside (i.e. with changing degree days). Currently the relationship between these two variables is understood only crudely despite that fact that temperature is a key driver of energy use in buildings. We don’t know how much scatter varies within a building class, what average scatter is, what proportion of buildings has high and low amounts of scatter, and what characteristics these buildings might have in common.
Through statistically analysing large datasets from SMEasure, we can answer these questions and in turn develop much better diagnostic tools. For example, as a consequence of research by the Lower Carbon Futures group, partly based on SMEasure data, a firm relationship between low scatter and better energy efficiency was established, providing a useful benchmarking metric for practitioners and researchers. This leads to much better feedback to you, our users. This sort of information also means we can better represent how different building types will use energy at different temperatures in large scale models.
3) How does the accuracy of energy and degree day data affect our diagnosis of performance curves?
By answering this question we can understand how important accuracy is in how we take energy meter readings and degree day readings. We can then better account for errors in our diagnostic tools, and inform building energy management guidelines.
4) How can we benchmark across buildings and building types?
Even within a class of buildings (such as retail) space is used very differently. For example, some supermarkets have a hot carvery area which will use quite a lot of energy, but doesn’t take up much space. Others will have a pharmacy, which might take up more space but use small amounts of energy. This makes it hard to compare energy use between these buildings in a meaningful way. Currently SMEasure users are defined by their CIBSE building class, but in the future we hope to provide sub-category information. This means we will be able to determine benchmarks across these sub-classes and in turn provide much more powerful feedback to users. This may also inform accreditation schemes such as Energy Performance Certificates.
Whilst SMEasure is giving researchers in Lower Carbon Futures lots of food for thought, the same researchers are also using their research insights from elsewhere to improve SMEasure so that it is a better tool to use. This includes:
Policy
The Lower Carbon Futures team considers ‘Market Transformation’ to be a central tool in helping to reduce greenhouse gas emissions from the UK’s building stock. This idea is that by using a mixture of approaches, including information (eg energy labels on buildings and equipment, feedback), incentives (such as stamp duty rebates for zero carbon buildings) and regulation (such as equipment minimum standards, building regulations or planning requirements), the building stock can be transformed over time to have lower average emissions.
SMEasure will provide better quality information on which to base energy labels, and by helping to develop more sophisticated models, recommendations from LCF to policy makers about the balance and form of incentives and regulation can be made on a better evidence base.
Collaboration
We have partnered with Julie’s Bicycle, a not-for-profit organisation seeking to green the creative industries with a focus on music, theatre and the visual arts. We are working with them to improve the benchmarks for theatres and music venues. Over 50 theatres and music venues are using SMEasure – this is resulting in valuable feedback to these organisations wanting to monitor and improve their energy performance.
Through the process of using SMEasure and the findings of associated research we are helping organisations comply with their regulatory and voluntary commitments.
We would very much like to collaborate with more organisations who share our vision and goals. Please get in touch if interested! Email: muriel@smeasure.org.uk
Our publications
Layberry, R. (2009) Improvements to the Meteorological Office equations in the computation of degree days. Building Services Engineering Research and Technology Vol. 30, No. 4, 357-362 DOI: 10.1177/0143624409343017
Layberry, R. (2009) Analysis of errors in degree days for building energy analysis using Meteorological Office weather station data. Building Services Engineering Research and Technology 2009 30: 79-86
Layberry, R. (2008) Degree days for building energy management - presentation of a new dataset. Building Engineering Research and Technology (In Press)
Bottrill, C. (2007) Internet-based tools for behaviour change Proceedings, European Council for an Energy-Efficient Economy 2007 Summer Study, France
Layberry, R. and Hinnells, M. (2007) Transforming UK non-residential buildings: achieving a 60% cut in CO2 emissions by 2050 Proceedings, European Council for an Energy-Efficient Economy 2007 Summer Study, France
