Degree days are the most underused performance metrics in energy data analysis. Simultaneously, they are widely misunderstood and therefore misused.

Weather conditions are the most influential factor when it comes to the variability of energy use in buildings. Even in a moderate climate such as the UK’s, changes in the requirement for heating and cooling typically accounts for 50%-70% of the variation in usage over the course of a typical year. In other words, regardless of everything else that’s going on inside or outside our buildings, changes in weather account for nearly all fluctuations we see day to day, week to week or month to month.

If you’re not going to use degree days for your energy data analysis, then you may as well be blindfolded. It would be like going on a diet but not tracking how much you eat.

Data-driven insights from Internet of Things (IoT) devices are enabling energy and sustainability experts to make better-informed decisions like never before. Energy use within buildings is inherently variable, and until recently, even the brightest minds were in the dark when it came to explaining fluctuations in usage, due to the long list of factors that can cause a building’s energy usage to change seemingly at random. Having a fundamental understanding of how a building uses energy and what causes it to change is essential to optimising performance in the long term.

Every experienced energy and sustainability professional who reads this blog, knows of course, that the first step of implementing energy management solutions, is acquisition of the building consumption data. This process, involves cooperation between different industry groups, and can often be long, complicated and costly.

In my last blog, I have shared my thoughts on why it is so important to access the right data when developing your Net Zero Carbon programme.

Lets dive deeper into the challenging process of energy data acquisition, and discuss ways to make it easier, more effective and more cost efficient.