Weather drives day-to-day variation in energy use that obscures underlying trends. Statistical analysis can help you pull a signal from the noise.
To understand your building, you need to know which way the wind blows
Weather drives day-to-day variation in energy use that obscures underlying trends. Statistical analysis can help you pull a signal from the noise.
Weather and energy use
Weather is one one of the dominant drivers of building energy use, and every building responds to outside conditions in its own way. Weather variability poses operational challenges, but it also poses a barrier to understanding. Seemingly random fluctuations caused by the interplay between air temperature, humidity, cloud cover and other factors mask other, subtler changes in your building.
Adjusting for weather effects
Weather normalization uses a statistical model to tease out the contribution of weather conditions to building energy use. Once the weather component has been characterized mathematically, it can be subtracted out of the daily load curve, yielding a “weather-normalized” picture of building energy use that more clearly shows underlying trends. Traditional weather normalization techniques create regression models of monthly bills using cooling degree-days (CDD) or heating degree-days (HDD) as inputs. Although conceptually similar, newer techniques use hourly weather data and the much more detailed energy use data available from smart meters to characterize building energy use for more accurately.
Why normalize for weather?
Weather normalization is the only way to answer questions like the following:
- What savings have we realized from our efficiency projects?
- Is the building experiencing operational drift?
- What is causing our budget variances?
A single question links these seemingly disparate questions: what portion of my building’s energy is due to weather, and what portion is due to other factors?