How do I interpret my building's energy performance line?
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A well run building should have an energy usage which is proportional to the number of degree days for same time period. To start to learn how well your building is running look at your performance chart - this is the first chart you see when you click the “weather analysis” tab on your buildings results page. This chart is a scatter plot of weekly degree days (x-axis) and heating energy consumption for that week (y-axis). When you set up your building you will have selected gas, electricity or oil as the heating source for your building and consumption here shows only for that type of fuel.
Performance lines can be calculated from 3 weeks or more data points (4 or more meter readings). They also require that the building floor area and heating source are set. We recommend that you carefully select a local weather station to improve the accuracy of this analysis.
If you hover your mouse over each point (red diamond) on the chart you can see the week date and energy use for that week. The most recent week always shows as a black diamond so you can quickly check how you are doing each week.
For information on building base temperature see this FAQ .
Three characteristic values are calculated from your chart:
Click here for worked examples
Slope
The slope of the best fit to the performance curve gives the energy loss per degree day, i.e. how much the building fabric and air tightness contributes to the energy used by the building. It is measured in kWh/degree/week. Higher values means more energy is being lost through the building shell for every degree colder the external temperature is.
A shallow gradient is good as it means energy consumption only increases a small amount for higher degree days. The building has low heat loss and is probably well insulated and has large gains due to high occupancy or lots of machines such as computers. The more flat your performance line the better the energy performance of your building.
A steep gradient is a sign that a building has high heat loss and that the heating system is working hard to heat the building. This maybe be due to poor insulation, cracks around windows and other draughts, windows and doors being left open.
A large building will consume more energy than a small building so high slope can be reconciled with overspend/underspend to see if the slope is unusually high for the building type and building size.
50% of buildings have a slope value below 0.1 kWh/m2/degree/week, 30% less than 0.08 and 10% less than 0.04.
- slope > 0.1 kWh/m2/degree/week worse than average – tend to use 30% more energy overall than the average
- slope < 0.1 kWh/m2/degree/week better than average – tend to use 35% less energy overall than the average
- slope < 0.08 kWh/m2/degree/week much better than average – tend to use 45% less energy overall than the average
- slope < 0.04 kWh/m2/degree/week excellent – tend to use 55% less energy overall than the average
A slope less than zero implies the building uses less energy as the temperature is colder and must be an artefact due to poor curve fitting because of high scatter or very bad control systems.
Baseload
The point where the performance line intercepts the y-axis indicates the baseload, which is the energy use that is independent of external temperature. This will include hot water heating, cooking or other appliances.
The baseload of a building is not an indicator of energy performance for the fabric of the building and will depend largely on what the building is used for. A warehouse with machinery inside that operates 24 hours will have a very high baseload compared to an office which is occupied 8 hours a day, 5 days a week.
A large building will consume more energy than a small building so high slope can be reconciled with overspend/underspend to see if the baseload is unusually high for the building type and building size.
The value of the baseload is not important in sMeasure, what is important is that it does not fluctuate wildly and that it does not increase without explanation.
R value
The R value is a goodness of fit statistic which is the measure of the correlation between degree days and energy used. An R value of zero shows that external weather conditions do not affect the energy used in the building whilst an R of 1 shows that the energy used in the building perfectly follows the weather (and that all the data points lie on the line).
A high value of R is evidence of a good control system in a building and a low value can imply control problems. A low R value may be due to a problem with the heating system such as a faulty thermostat or problems circulating heat around the building. Large variation in baseload can lead to low scatter as can variation in occupancy (e.g. in a school which is closed during the holidays.)
You should also check that you have selected the closest local weather station to your building so that the most appropriate value of degree days is used in your analysis.
You can hover over any points on the chart which lie far away from the line and try to work out what was different in your building for that week compared to other weeks.
Recent work has shown that for an R value above 0.7, two months of data is sufficient to gain a yearly benchmark within 10% of actual annual energy use.
The mean R value for a building is 0.87 with 30 % having an R value greater than 0.93 and the best controlled 10% of buildings having an R value greater than 0.97
Buildings with the highest R values use considerably less energy than those with lower R values, due to the better level of control and less wasted heat. Buildings with an R value over 0.9 use 10% less energy on average, those over 0.95 use 15% less and those over 0.97 use 20% less energy on average.
- R < 0.87 below average
- R > 0.87 above average
- R > 0.93 well above average
- R > 0.97 excellent
Examples
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| This is an example of high correlation, the points lie close to the line of best fit suggesting the building has good energy management. By hovering the mouse over the obvious outlying point we can find out the week of this abnormally low consumption and work out why this occurred. |
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| This is an example of poor scatter and is reflected by the much lower R value compared to the building above. |
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| 0.127 kWh/m2/week.This graph has a lower slope compared to the graph below. However this building has a floor area of 2895m2 whereas the building below has an area of 16,700m2. This means that actually the kWh/m2/week is lower for the graph below. The lesson here is that a high consumption must be put into context with building area and type. |
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| 0.011 kWh/m2/week |
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| This buildings performance line shows a negative slope. It does not make sense that a building would use less energy to heat if it was colder outside so there might be a problem with the consumption data here, or the local weather station may be incorrectly set. |
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| If meter readings are not taken regularly (weekly is best) then the system has to interpolate lots of readings. When multiple readings are interpolated the same consumption is recorded for each week, when there were a different number of degree days for each of those weeks. This leads to horizontal lines of points on the chart which lead to inaccuracy and reduce the R value |
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| Here we see a chart that looks normal, apart from having a large, negative baseload. Whilst there may be errors in the data this could also be an effect due to a high slope. |