Four Easy Steps to using Degree Day Reporting Effectively
Read Mark Dobson's article on degree days. In just four easy steps he explains how to achieve best practice monitoring of your temperature dependent energy consumption.
When energy consumption is affected by external temperature changes, your analysis should take temperature into account. Our latest degree day reports allow you to normalise for temperature when benchmarking your sites and continuously track performance against intelligent targets.
Here are our recommended four steps to achieving best practice monitoring of your temperature dependent consumption with our new degree day reports.
1. Rank your sites by base temperature
Determining the correct base temperatures for your building is an important initial step in the degree day reporting process. This analysis can also give you immediate insight into your site’s performance.
The higher the heating base temperature (HBT), the higher the external temperature at which you require heating. For example, a site with a high HBT might need to be heated to a higher than normal internal temperature or it could indicate poor insulation, faulty temperature sensors or inadequate controls.
2. Examine the relationship between your consumption and degree days
The colder the outside temperature, the more energy your building is likely to need to reach and maintain a comfortable internal temperature. Equally, the higher the external temperature, the more energy your building is likely to need for cooling.
Regression analysis is used to examine the relationship between two or more variables. The scatter graph above shows an example of the interaction between degree days and consumption. Each data point represents the consumption and degree days in a set period. This set period could be a day, a week or a month’s worth of data; often I use week figures as the sites I’m analysing will have a weekly pattern of consumption and this provides a sufficiently large data set without having to wait more than a couple of months.
How closely your data fits a line of best fit, often referred to as the ‘performance line’, can indicate how strong the relationship between your heating related consumption and the external temperature is.
We want to find the sites where consumption is not showing a strong relationship with degree days when it should be. To do this we can use something called the r2 value. These sites are not showing a correlation between consumption and degree days. This in turn could indicate a heating system that isn’t reacting to outside temperature changes which can often be an indication of wasteful energy consumption.
3. Check out the slopes!
The line of best fit or ‘performance line’ can be used to give further insights into temperature related consumption.
For example, the slope or gradient shows the consumption required for each additional heating degree day. Sites with a higher gradient are consuming energy at a greater rate per degree day.
The image below shows some of the other insights you can gain from the performance line.
When working with a customer who manages a large portfolio, I first focus my attention on the least efficient sites. Luckily, Stark has turned the techniques above into new reports. These not only do the regression analysis for you but rank your sites by either the r2 value, the gradient of the performance line or a KPI that normalises for your site’s floor area (larger sites tend to require additional consumption to increase internal temperatures).
4. Track against what’s expected
After identifying the sites to target first, you’ve hopefully managed to tackle the energy waste by taking measures to improve the controls, building fabric or heating-equipment at site. The next step is to track your consumption going forwards.
You can do this by using your regression analysis and performance line to estimate an expected consumption based on the number of degree days in a set period.
This expected consumption figure can be compared with actual consumption during that period allowing you to track changing performance. The temperature dependent exception report on SavenergyOnline checks a week’s worth of data and will be automatically delivered to you when your consumption exceeds that which is expected.
I have found great advantages in using the techniques above to find waste and make savings, and there are many more ways to use degree days when analysing temperature dependent consumption. Give our new reports a go and if you need any advice then get in touch.