COLIN PARRY
MEASURING THE EFFECT
OF CHANGE ON PHYSICAL ASSETS COLIN PARRY is the founder of Head for Data, a data consultancy based in Scotland. Prior to this he was Director of Data Science at arbnco, where the work described below was developed. He has worked in the energy field for 14 years using data to drive innovation and improve business outcomes. He has worked on a diverse range of projects throughout his career and holds four patents in the area of energy management in buildings.
TRADITIONAL CHANGE MEASUREMENT PROCESSES How do you measure the effect of a change? Ask a data scientist this question and they are likely to think of A/B testing. An A/B test is a form of randomised control experiment in which multiple measurements are taken of a metric, but this is acquired from two different groups with a slight variation in user experience. For example, a website might measure click-through rate whilst slightly varying elements on a page, showing one group a red button and another group a green one. A drug trial might measure the recovery rate from illness whilst slightly varying the medication given to each group, giving one group a tablet with an active ingredient and another group a placebo. This is now an industry standard way of measuring the effectiveness of a change. But there are some implicit assumptions: 1. It must be physically possible to get samples from multiple groups simultaneously. 2. The marginal cost of acquiring new samples cannot be prohibitive. 3. Each sample must be comparable inside the group and across groups. Serving up a new website layout only requires a few configuration changes and the cost of acquiring multiple groups is negligible, therefore this is an easy experiment to run. In the case of a drug trial, this is trickier as more people are required to participate so the marginal cost
is higher, resulting in generally smaller sample sizes, but still enough to make the results meaningful. In both examples assumptions about the people involved need to be made – a website may segment visitors by interests or affluence to ensure the groups are comparable. The drug trial may limit participation to those with a certain BMI or age to reduce the effect of these latent variables on the results. What happens, though, if the thing being measured breaks all three of these rules? What if the measurement takes place on an asset that is one-of-a-kind, and it costs thousands of dollars to make a change?
ENTER BUILDINGS An oft-quoted statistic in building science is that 80% of the buildings that will be occupied in 2050 already exist today[i]. Globally, buildings account for 40% of our energy consumption and 33% of our greenhouse gas emissions[ii]. Unlike areas such as transport and technology, reducing the energy consumption from the buildings we all live and work in cannot be primarily achieved by building cleaner and more efficient products. Clearly, new buildings will be more efficient than those already built, but for the large stock of already constructed buildings we need to rely on retrofits. A retrofit to a building is anything that changes something already existent in the building. Switching to LED lightbulbs, adding insulation and changing out a HVAC or boiler to a more efficient alternative are all examples of retrofits aimed at reducing energy consumption. As well as the environmental benefit
28 | THE DATA SCIENTIST
