Bulletin 4 May 2021: building insulation; U is for uncertainty

Good morning


This two-hour technical briefing on 19 May covers the materials and techniques currently specified for improving the thermal performance of buildings. It includes revision of basic principles, discussion of the limitations of each type of product, and a summary of relevant UK regulations and standards. Details are at https://vesma.com/z112 and as ever your readers’ discount code is EMR2012.

Other forthcoming events are listed at http://vesma.com/training.



When we plot energy consumption against driving-factor values on a scatter diagram, the points don’t fall exactly on the regression line. The degree of dispersion is described by a parameter called the ‘coefficient of determination’, commonly known as R-squared, which tells us how much of the variation in energy consumption is explained by the regression model. When all the points fall exactly on the line, the model explains all the variation in energy consumption and R-squared has a value of one. If there is no relationship between consumption and the chosen driving factor, R-squared would be zero. If R-squared is 0.9 it means that the model explains 90% of the observed variation in energy consumption with the remaining 10% being attributable to errors or factors that were not taken into account.

There are two common misconceptions about R-squared. One is that on a heating system, a low value of R-squared signifies poor control. This is not necessarily the case, as the following thought experiment will show. Consider a well-controlled heating system whose consumption is assessed against a reliable local source of degree-day data. Whatever value of R-squared is observed, if you were to substitute degree-day statistics from a more distant weather station in the regression analysis, R-squared would go down, even though the heating system continues to be well-controlled. So beware: low R-squared might be telling you more about the quality of the model and your data than about the behaviour of the thing you are monitoring.

The other common misconception is that there is a threshold for R-squared (0.75, or 0.9, or whatever) below which your regression model cannot be trusted. There is no such cut-off. If you have chosen the most relevant driving factor and a straight-line model is plausible, you have got the right model and a low R-squared value just means it is not as reliable as it could be. In practice that simply means that a deviation has to be bigger before it can be treated as something that didn’t happen by chance. By refining the model you will improve your ability to discriminate between real faults and random variation. So it’s not a question of do you trust the model or not; the question is: “given a plausible model, how much uncertainty is there in its predictions?”. Hence the idea, introduced in an earlier bulletin, of tuneable +/- control limits on charts showing the history of deviation from expected consumption.

In the next issue: V is for verification of savings. Meanwhile if you missed any earlier issues you can catch up at http://EnManReg.org/azmt



We’ll be running our measurement and verification conference again as a series on Wednesday afternoons starting on 20 October. By popular request the first two sessions will be a refresher on basic principles and good practice, and the final sessions will be aimed at advanced practitioners but open to all. If you have an idea for a presentation (even if you don’t want to do it yourself) please let me know now.

Kind regards