Retrotec USACE - Manual (Page 85)

Chapter 7 63
7.5.2 Interpretation of Monitored Data 1: Intelligent Monitoring
Intelligent monitoring is the most popular way to analyze measured data. The
data are visualized in different contexts to allow people to interpret them easily.
Such contexts include
Comparison of yearly data from similar buildings to perform bench- ■
marking
Comparison of temperature corrected yearly data from one building
■
and several years to control system development and maintenance
Comparison of temperature corrected monthly data from one building
■
and several years to visualize consequences of ECM
Comparison of monthly data from one building with outdoor tempera-
■
tures to prove that the temperature control is active
Comparison of daily data from one building with outdoor temperatures
■
to prove that weekend setback is active
Comparison of hourly data from one building with outdoor tempera-
■
tures to prove that night setback is active
Comparison of yearly, monthly, daily, or hourly data from different
■
units (e.g., apartments) of one building to make the user behavior more
transparent
Comparison of yearly, monthly, daily, or hourly data from different units
■
(e.g., rooms) of one building unit to make the behavior of inhabitants
more transparent
Energy signatures typically display energy consumption versus outdoor air
temperature. The characteristics of an energy signature (inclination, change
points, intercepts) can be used for gross fault detection.
Scatter plots, or XY plots, can help to visualize the dependency of two vari-
ables. Energy signatures are typical XY plots. Note that time dependency is lost
in XY plots.
Carpet plots are a special way to visualize one variable over a period of
time, especially if the time period is long. They can be displayed for several
months or even a whole year. The value of the variable is displayed as a color.
Thus, for example, plotting an on/off control signal produces a typical pattern
that is easy to recognize.
Box plots (also known as a box-and-whisker diagram or candlestick chart)
are a way to display statistical data analysis in a graphical way.
Scatter, carpet, and box plots are especially suited for analyzing because
they reveal the characteristics of the energy consumption and the system tem-
peratures.
Classical time series plots, however, are a valuable reference for a closer
examination of the time dependency.
Important additional features of visualization are fi lters and groupings.