Certified Energy Auditor Energy Audit Data Analysis Questions and Answers

Question 1

An energy auditor develops a linear regression model to predict a building's monthly electricity consumption based on cooling degree days (CDD). The analysis yields an R-squared (R²) value of 0.85. How should the auditor interpret this R² value?

Accepted Answer

85% of the variation in electricity consumption can be explained by the variation in cooling degree days.

Answer

R-squared (R²), or the coefficient of determination, is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. In this context, an R² of 0.85 means that 85% of the variability in the building's electricity consumption is accounted for by the changes in cooling degree days. It does not directly translate to cost prediction accuracy, equipment efficiency, or the probability of a correct prediction.

Question 2

A facility's utility bill for a 30-day period shows a total consumption of 360,000 kWh and a peak demand of 800 kW. What is the load factor for this period?

Accepted Answer

62.5%

Answer

The load factor is calculated by dividing the total energy consumed (kWh) by the product of the peak demand (kW) and the number of hours in the period. The formula is: Load Factor = Total kWh / (Peak kW * Hours in Period). For a 30-day period, there are 30 days * 24 hours/day = 720 hours. Calculation: 360,000 kWh / (800 kW * 720 hours) = 360,000 / 576,000 = 0.625, or 62.5%. A higher load factor generally indicates more efficient use of electrical capacity.

Question 3

An energy auditor is analyzing the natural gas consumption of a building over a year. To compare the heating efficiency between a mild winter and a severe winter, which of the following data analysis techniques is most appropriate?

Accepted Answer

Weather normalization

Answer

Weather normalization is the process of adjusting energy consumption data to account for variations in weather conditions, such as temperature. By using metrics like Heating Degree Days (HDD), an auditor can compare energy use across different time periods under a common set of weather conditions, allowing for a fair assessment of efficiency improvements or changes. Load duration curves, power factor, and data logging are useful tools but do not directly address the need to compare efficiency across periods with different weather.

Question 4

In a CUSUM (Cumulative Sum of Differences) chart used for energy monitoring, what does a consistent, steep, upward-sloping line indicate?

Accepted Answer

A period of persistent over-consumption of energy relative to the baseline.

Answer

A CUSUM chart plots the cumulative sum of the differences between actual and expected energy consumption. A flat, horizontal line indicates performance is matching the baseline. A downward slope indicates savings (less consumption than expected). A consistent, upward-sloping line signifies that actual consumption is persistently higher than the expected baseline, indicating a period of over-consumption or waste.

Question 5

An energy auditor needs to calculate the Heating Degree Days (HDD) for a day with a high temperature of 55°F and a low temperature of 35°F, using a standard base temperature of 65°F. What is the HDD for that day?

Accepted Answer

20 HDD

Answer

Heating Degree Days (HDD) are calculated by subtracting the day's mean temperature from a base temperature (commonly 65°F). First, find the mean temperature: (55°F + 35°F) / 2 = 45°F. Then, calculate the HDD: 65°F (base) - 45°F (mean) = 20 HDD. If the mean temperature were above the base temperature, the HDD would be zero.

Certified Energy Auditor Certification Practice Test

Certified Energy Auditor Certification Practice Test

Certified Energy Auditor Energy Audit Data Analysis Questions and Answers