Presentation for Data Science Melbourne meetup

1. Smart buildings
Cameron Roach
9 November 2017

2. Overview
Smart buildings
Analysing the data
Building an end product
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3. Smart buildings
Trying to be smart with commercial building data

4. How do commercial buildings work?
Facility managers (FMs) oversee the day to day operations of a commercial
building.
Used to be a whole team!
Shrinking maintenance budgets and increasing complexity makes this a
challenging problem.
Building operations are automated by Building Management Systems (BMSs).
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5. What is a BMS?
Commercial buildings contain Building Management Systems (BMSs) to
improve indoor environment quality and reduce energy consumption.
A BMS will control heating, cooling, ventilation and lighting systems.
Contain thousands of points for sensors (temperature, humidity), actuators
(fans, motors, dampers) and software (schedule, trend logs, calculations).
A BMS will monitor sensors and adjust actuators based on their readings.
For example, if high temperatures are recorded in a room, dampers will open
and air handlers will modulate to provide cooler air.
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7. How does this work in practice?
Vendor sets up a BMS. The BMS will behave in a certain way based predefined
rules.
BMS systems are costly to implement and to modify. Can require a lot of
coding to change the BMS's behaviour.
The bigger the BMS is the harder it is to find what matters. Locating problems
is difficult and time-consuming.
For example, a heating valve might be locked open. If this isn't detected the
BMS will cool the room to reach the required temperature.
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8. So what can we do?
Help facility managers identify if a BMS is operating optimally.
Buildings Alive's goal is to
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Fault detection
Diagnostics
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Collect BMS data using our E2 device
Analyse and transform data into useful information.
Help guide FM's to find out what's wrong.
Provide timely and actionable information.
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9. Analysing the data
Feature generation, dimensionality reduction, clustering

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12. Feature generation
Dealing with thousands of unevenly spaced time-series.
Uneven spacing in time-series presents difficulties.
Rather than rounding or imputing data we can generate features and work
with them instead.
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13. What features might be useful?
Feature generation for time-series clustering is discussed in Wang, Smith, and Hyndman (2006). Some
useful features for our case might be
Normalise these features using their median, , and interquartile range, ,
Mean
Standard deviation
Kurtosis
Skewness
Biggest change ( )
Smallest change ( )
Number of "mean crossings" per day
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· { − }maxi
∣
∣yti
yti−1
∣
∣
· { − }mini
∣
∣yti
yti−1
∣
∣
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M IQR
= .y
∗
y − M
IQR
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16. Dimension reduction and clustering
Too many sensors to visualise easily.
Use dimensionality reduction.
Identify clusters and singletons.
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17. Which clustering algorithm?
Method Advantages Disadvantages
K-means Easy to learn. Outperformed by other algorithms.
Hierarchical clustering
Informative - produces a
dendrogram.
Not suitable for large data sets -
time complexity.
Affinity propagation
Automatically determines number of
clusters.
Not suitable for large data sets - time
complexity.
Spectral clustering Good performance.
See Nadler and Galun (2007). Time complexity
of .
( log(n))n
2
( t)n
2
( )n
3
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18. Image: (“Comparing Different Clustering Algorithms on Toy Datasets” 2017)
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19. Obligatory mathematics slide
Spectral clustering
We are given points and a similarity matrix . Define the weight matrix, degree matrix and
graph Laplacian as
where,
Once is determined find the eigenvectors corresponding to the smallest eigenvalues of .
Finally, cluster the rows of using K-means.
n ∈xi ℝ
p
S
W
D
L
= ( ) ∈wij ℝ
n×n
= diag ( )di
= D − W,
is the weight between nodes and based on , and,
is the weighted degree of node .
· wij i j S
· =di ∑
n
j=1
wij i
L m Zn×m m L
Zn×m
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20. Building a prototype
Prototyping with Dash

21. Dash
Recently released by Plotly.
Easily build web applications for
data analytics.
Open sourced under the MIT
license.
Works nicely with the existing Plotly
graphing libraries.
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Python equivalent of R's Shiny.·
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23. Simple example
import dash
from dash.dependencies import Input, Output
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
app = dash.Dash()
app.layout = html.Div([
dcc.Dropdown(id='my-dropdown',
options=[{'label': 'Option A', 'value': 'A'},
{'label': 'Option B', 'value': 'B'}]),
dcc.Graph(id='my-graph')
])
@app.callback(Output('my-graph', 'figure'),
[Input('my-dropdown', 'value')])
def update_graph(dd_value):
df_query = df.query("Variable == @dd_value")
return {'data': [{'x': df_query.x, 'y': df_query.y}]}
if __name__ == '__main__':
app.run_server()
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24. Demonstration
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25. Thank you for listening!
Any questions?

26. References
“Comparing Different Clustering Algorithms on Toy Datasets.” 2017. http://scikit-
learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html.
Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. 2001. The Elements of
Statistical Learning. Vol. 1. Springer series in statistics New York.
Murphy, Kevin P. 2012. Machine Learning: A Probabilistic Perspective. MIT Press.
Nadler, Boaz, and Meirav Galun. 2007. “Fundamental Limitations of Spectral
Clustering.” In Advances in Neural Information Processing Systems 19, edited by P B
Schölkopf, J C Platt, and T Hoffman, 1017–24. MIT Press.
Von Luxburg, Ulrike. 2007. “A Tutorial on Spectral Clustering.” Statistics and
Computing.
Wang, Xiaozhe, Kate Smith, and Rob Hyndman. 2006. “Characteristic-Based
Clustering for Time Series Data.” Data Mining and Knowledge Discovery 13 (3): 335–
64.
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