Post on 21-May-2020
Copyright 2012
Introduction to Data Analysis
CEE 243
CEE 243:Week 4
Introduction to Data Analysis
Copyright 2012
Introduction to Data Analysis
CEE 243
Big ideas
• Reason to collect or display data: enable action
– Important to clarify who should/could take action
• Energy analysis requires lots of data, and there
are multiple ways to display and manipulate
data. This session shows you
– Examples of lots of data
– Examples of different good ways to show data:
scatter plots, carpet plots, commercial examples
– Good ways to show and annotate data to support
action
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Copyright 2012
Introduction to Data Analysis
CEE 243
Poor data display prevents the communication of
information to the intended audience
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What is wrong with this image?
Copyright 2012
Introduction to Data Analysis
CEE 243
Poor data display in the Building Performance
Domain wastes time and money
• Obvious Clutter
• Terrible Time scale
• Poorly positioned Legend
• Poor choice of scale for second Y axis
• No so what
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Copyright 2012
Introduction to Data Analysis
CEE 243
Tufte’s principles of Best Practice Data Display enable
unambiguous communication of information
• Integrity of Data
• Enforce Visual Comparisons
• Show causality
• Show Multivariate Data
• Integrate words, numbers and images
• Design should be content driven
Suggested Reading: “The Visual Display of
Quantitative Information” by Tufte
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Copyright 2012
Introduction to Data Analysis
CEE 243
Interesting (Amazing!) Visualization
• http://www.ted.com/talks/hans_rosling_shows_t
he_best_stats_you_ve_ever_seen.html
• Minutes 1-12
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Copyright 2012
Introduction to Data Analysis
CEE 243
Impressive data display for a large volume of
energy information
Shows geography plus intensity (Howard 2012) 7
Copyright 2012
Introduction to Data Analysis
CEE 243
Best Practice Data Display
• Shows geography plus time variance in army size, army
movement, temperature
• Methods: – Labels
– Line size
– Line location
– Direction - color
– Date – annotation
– Temperature
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Courtesy of Tufte Charles Minard's flow map of Napoleon's March, http://en.wikipedia.org/wiki/Charles_Joseph_Minard
Copyright 2012
Introduction to Data Analysis
CEE 243
Jargon
• Lie factor = “the size of an effect shown in a graph
divided by the actual size of the effect in the data on
which the graph is based” – Source: http://www.infovis-wiki.net/index.php/Lie_Factor
• Example:
– Size of Gas economy effect in graph (right) = (5.3 inches –
0.6”) / 0.6” = 783%
– Size of time (left) effect in data = (27.5 – 18) mpg / 18 mpg =
53%
– Lie factor = 14.8
9 [Tufte, 1991]
Copyright 2012
Introduction to Data Analysis
CEE 243
Jargon
• Range frame: Data frame that begins and ends at the minimum
and maximum values respectively
– Minimum not 0
10 [Tufte, 1991]
Copyright 2012
Introduction to Data Analysis
CEE 243
An importance checklist to help ensure clear graphs
Grey grid
Active grid lines
Range frame – only very carefully
Graphs must be at the correct magnitude
Use graphics that are proportional to data: Lie Factor ~ 1
Option to turn grid on and off
Golden rectangle (ratio of length to height of approximately 1.6:1)
Use clear, thorough labels -- not of vertical orientation;
Label important features and so-whats in the data itself
Maximum number of data streams is six
Sans Serif font
White background, important for Excel graphs
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Copyright 2012
Introduction to Data Analysis
CEE 243
Annotation of figures
Two crucial parts to a caption or figure
annotation:
1. Features: important geometric content of the
figure or chart: axes, colors
2. Significance: information that the figure
contains and its significance for the reader
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Copyright 2012
Introduction to Data Analysis
CEE 243
Fundamental Performance Analysis
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Copyright 2012
Introduction to Data Analysis
CEE 243
Analysis of a typical work day shows a recognizable
pattern • Features
• Significance:
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Copyright 2012
Introduction to Data Analysis
CEE 243
This site load for this campus has recognizable pattern
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• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
Analyzing multiple days or weeks enables further
investigations
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A The load profile shows that overnight setbacks were relaxed. B AM peaks are far in excess of the midday peak, leading to excessive demand charges.
Granderson 2010, Energy Management handbook
• Features
• Significance:
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Introduction to Data Analysis
CEE 243 17
Carpet plots help identify patterns in large data
sets
• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
Monthly totals enable rudimentary comprehension of
building performance and annual benchmarking
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• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
Benchmarking has many forms, which is most
appropriate for you?
• At the site or building level – Utility Values
• Compare with last year
• Normalized comparison – Commercial Website name Cal Arch
http://poet.lbl.gov/cal-arch/
– Home Version also home energy saver
http://hes.lbl.gov/
• GSA Building Performance Tool Kit (Augenbroe)
• Optimum Performance Benchmarks??
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Copyright 2012
Introduction to Data Analysis
CEE 243
Jargon
Degree Day Analysis: A unit of measurement equal to a difference of one degree between the mean outdoor temperature on a certain day and a reference temperature, – used in estimating the energy needs for heating or
cooling a building.
Reference Base Temperature =
15.5oC in the UK (60o F)
18.3oC in the US (65o F)
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Copyright 2012
Introduction to Data Analysis
CEE 243
Sample Degree Days:
American Locations and Heating Demand
• Table Illustrates number of Degree days for Variable Tbase (ASHRAE)
Copyright 2012
Introduction to Data Analysis
CEE 243
Degree Day Sample
Example of United States Heating Degree Days V Gas Consumption
• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
CEE 243 Data Analysis
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Copyright 2012
Introduction to Data Analysis
CEE 243
Process for data analysis in CEE 243
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Select system & points for analysis
Synthesize functional intent and data context
Access and graph data
Assess data conformance to functional intent
Identify potential causes
Document and discuss findings
Step 1
Step 4
Step 2
Step 3
Step 5
Step 6
End
All Ok
All Ok
All Ok
All Ok
All Ok
All Ok
Steps 1-5 to Clarify
Else
ElseSteps 1-4 for
satisfactory fault diagnosis
Steps 1-3 for additional
clarity
Else
Else repeat Step 3 for additional clarity
Copyright 2012
Introduction to Data Analysis
CEE 243
Simple But Effective Visualization
Can you improve it?
• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
Effective Visualization:
CV Box 2-1-3 Offices 297,295 and 293
Functioning properly Suspicious Behavior (Revise) Problem (Immediate attention)
• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
Effective Visualization?
Radiant Floors
High Slab Heat •Radiant Heat On/Off?
•Sensor or Valve Failure?Test?
Valve Closed: •OAT>78°
High Slab Heat •But valve closed°
•Sensor or Valve Failure? Test?
Valve position
change not gradual
Set Point at 72°, not
71°
Unoccupied
Space Temp
setpoint≠ 66°
Valve 120% Open? Not
Possible
• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
Effective Visualisation?
Natural Ventilation
Open Window: •Space Temp high
•OAT <Space Temp
Smoke Test •Once per week
Close Window: •Space Temp <70°
No Nat Vat: •68°<OAT<85
°
68°Min OAT Set Point
Window Closed
Window Open
Night Purge: •Day OAT>75°
•Night OAT<65°
•Space
Temp>65°
• Features
• Significance:
Copyright 2012
Introduction to Data Analysis
CEE 243
An importance checklist to help ensure clear graphs
In addition to today’s Slides
Traffic light green/yellow/red show assessed status
On Images, Annotations of anomalies (important features) on
which the developer wants a reader to take action, e.g., note a
times-10 conversion error
In Wiki, accompanying text to explain
Functional Intent
Functional Areas
Problems Areas (Describe annotated anomalies)
Recommended action
Labels on x and y axes
Names of graphed data
Engineering units of graphed data
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Copyright 2012
Introduction to Data Analysis
CEE 243
Other Visualizations
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Copyright 2012
Introduction to Data Analysis
CEE 243
These links contain thought provoking visualizations
http://vis.stanford.edu/files/2010-Narrative-InfoVis.pdf
http://www.cbe.berkeley.edu/centerline/winter2009.pdf
http://www.cs171.org/2008/final_project/web_pages/wright_w/wright-
w.htm
http://www.nytimes.com/interactive/2010/06/29/magazine/rivera-
pitches.html
http://spotlight.abs.gov.au/Flash/#/2/0
http://vanderlin.cc/projects/mbta_visualizations
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Copyright 2012
Introduction to Data Analysis
CEE 243
This slide promotes the assertive evidence based
approach to powerpoint presentations
http://www.writing.engr.psu.edu/slides.html
No bullets
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http://hosting.epresence.tv/LBL/1/Page/Home.aspx?=&page=1
Leader Alumni 1/27/12
Copyright 2012
Introduction to Data Analysis
CEE 243
Big ideas
• Reason to collect or display data: enable action
– Important to clarify who should/could take action
• Energy analysis requires lots of data, and there
are multiple ways to display and manipulate
data. This session shows you
– Examples of lots of data
– Examples of different good ways to show data:
scatter plots, carpet plots, commercial examples
– Good ways to show and annotate data to support
action
33