ERAC Energy Data Analytics & Reporting Flashcards
6 cards from real ERAC practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 ERAC Energy Data Analytics & Reporting flashcards as text
Which ASHRAE standard provides guidelines for energy auditing levels (Level I, II, and III)?
Answer: ASHRAE 211
ASHRAE Standard 211 establishes the three-tiered framework for commercial building energy audits, ranging from a simple walkthrough to detailed energy simulations.
What statistical measure is used to evaluate how well a regression model fits the energy baseline data?
Answer: Coefficient of Variation of Root Mean Square Error (CV-RMSE)
CV-RMSE is the standard metric used in IPMVP and ASHRAE Guideline 14 to evaluate baseline model quality, with values below 25% typically indicating an acceptable fit.
In energy data management, what is the significance of 'interval data' from a smart meter?
Answer: It provides time-stamped consumption readings at frequent intervals (e.g., 15-minute)
Interval data captures energy consumption at frequent time intervals (typically 15 or 30 minutes), enabling detailed load profiling and identification of operational patterns.
What is 'phantom load' (also called standby power) in energy data analysis?
Answer: Electricity consumed by devices while in standby or off mode
Phantom load refers to electricity consumed by electronic devices while they appear to be off or in standby mode, which can represent 5–10% of a building's total consumption.
Which data visualization technique is most effective for identifying recurring energy waste patterns across time-of-day and day-of-week dimensions?
Answer: Heat map (calendar chart) of energy intensity
A heat map displaying energy intensity across hours of the day and days of the week is the most effective tool for identifying recurring waste patterns such as after-hours operation.
In the context of energy reporting, what does 'normalization' primarily achieve?
Answer: Adjusts energy consumption data to remove the effect of external variables like weather or occupancy
Normalization adjusts raw energy data to remove the influence of external variables such as weather, occupancy, or production levels, enabling fair comparisons across time periods or facilities.