#### What is in this article?:

- Analyzing Building Energy Use
- Cooling Energy
- Obtaining Degree-Day Data

The ability to analyze utility bills allows an engineer to understand HVAC energy use and find ways to use energy effectively in buildings.

**Cooling Energy**

**Table 2** presents electricity-use data for the building.

**Figure 2** is a plot of electricity use against HDD. Once again, the solid line is the least mean squares linear regression line. The graph and the regression line lead to the following observations and analysis:

• The slope of the cooling-analysis graph is opposite that of the heating-fuel-analysis graph. It is no surprise that HDD go down as outdoor temperature goes up, and cooling load generally goes up as outdoor temperature goes up.

A similar plot could be made with cooling degree days (CDD) on the x-axis. CDD are calculated the same way HDD are (base temperature minus average daily temperature). When electricity use is plotted against CDD, the trend normally is upward to the right—electricity use increasing as CDD and outdoor temperature increase.

The correlation coefficient (R^{2}) of 0.84 for the example building is not as good as the heating-energy correlation coefficient, but is not bad for a cooling analysis. Cooling-energy correlations almost always are lower than heating-energy correlations because factors other than outdoor temperature (e.g., internal heat gains, solar heat gain, humidity) affect cooling load. While solar heat gain and humidity usually rise with outdoor temperature, the relationship is only general. For some buildings, analysis based on cooling degree hours yields a correlation that is quite good. Cooling degree hours are calculated as the difference between hourly temperature and base temperature—no averaging required. Some experimentation might be required to determine the base temperature that produces the best correlation for the building being studied. The workable cooling-degree-hour base temperature might be 55°F, 57°F, 60°F, or even 65°F, depending on the type of building and its use.

• The graph shows a “knee” in the trend at about 600 HDD. There, the trend turns from downward sloping to nearly horizontal. That shows the building has near zero mechanical-cooling load at 600 HDD. A 30-day month produces 600 HDD at an outdoor temperature of 45°F (65°F − [600 ÷ 30]). An analyst might ask why mechanical cooling should have to run at an outdoor temperature of 45°F. The cooling system for this building uses multiple rooftop units. The apparent need to run mechanical cooling at an outdoor temperature of 45°F suggests a look into economizer operation would be justified.

• As with heating energy, the equation for the regression line makes separating mechanical-cooling electricity use from base electricity use possible:

kwh per month = 74,212 kwh per month − 58.191 × HDD per month

The knee in the graph shows no cooling-energy use above 600 HDD per month. Therefore, base electricity use is:

74,212 − (58.191 × 600) = 39,297 kwh per month

Rounding to 40,000 kwh per month, annual base use is:

12 × 40,000 = 480,000 kwh

Annual electricity use from the customer’s utility bills is 610,000 kwh, so cooling accounts for 130,000 kwh. That calculation is consistent with the regression-line slope and number of HDD (2,226) from February through November (the months with fewer than 600 HDD):

58.191 kwh per degree day × 2,226 degree days = 129,533-kwh cooling use

Because the electricity-use line is nearly horizontal above 600 HDD, the data show the building has little or no electric-heating load.