What is the control chart telling me?
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The purpose of the control chart is to give you some feel for the fluctuations in your building energy performance. The control chart visually displays how your actual weekly energy consumption varies from your predicted energy consumption.
The chart helps you quickly detect if something might be amiss with your heating system or controls.
This FAQ will explain:
Click here for worked examples
How the control chart is calculated
The control chart plots actual energy consumption minus predicted consumption over time for your heating fuel.
Predicted energy use is calculated week by sMeasure. The system uses the baseload and slope from your performance chart line, along with the degree days for that week. The point on your control chart is then calculated by subtracting your predicted consumption from the actual consumption. The weekly point is expressed in kWh above or below zero. The zero point is where actual energy use = predicted energy use.
How to interpret the control chart
When the value is positive, you have used more energy than predicted and when it is negative you have used less than predicted. If you have made a change to your building or heating controls and have saved energy this should show up as negative readings.
A control chart's line will naturally fluctuate above and below zero because energy use in buildings changes and is difficult to predict exactly. Weather and behavioural changes week to week will cause small fluctuations. We want to know when this fluctuation is more than expected as this suggests there may be a bigger or longer term problem (for example, a window is stuck open or the boiler is set incorrectly and over-heating). There are statistical methods for determining 'expected' levels of variation, however, at the moment in sMeasure we consider a variation threshold within plus or minus 20% as good.
Examples
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| This control chart shows a building with low variation, which is good. These small fluctuations will likely be due to weather and behavioural variations which are beyond control. |
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| This is a classic example of what you would see if part of your heating system were to fail. Initially the building is performing better than expected and using increasingly less energy than predicted. Suddenly there is a jump in usage which is sustained. If you check your chart weekly and spot something like this it would be a good idea to check your system for faults. |
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| Over a period of months this building has reduced fluctuations leading to better control. This may have been done by assessing equipment in the building and ensuring it was working and set correctly or by addressing occupant behaviour. |
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| Here we see a building where energy use is always above what is expected. This is not good and indicates poor control. As the predicted values are dynamic and the values used to calculate them each week change this in fact shows that the problem of over consumption has gotten worse over time. |
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| In contrast to the chart above, this building has consistently used less energy than predicted. As the predicted values are dynamic this shows measures have been consistently implemented and have reduced consumption week on week. |
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| This building started out with consistent weekly over-consumption. Over time, with implementation of better control measures, the building has started to perform better and consume less than predicted. |
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| In this chart there are fluctuations that are not weekly but more periodic. This could be caused by changing occupancy (e.g. school terms) or by the heating system itself. Settings may have been changed or parts may have broken and been changed. In this case the fluctuations are very large so it is important to find the cause as quickly as possible! |