Deep Learning (DL) is a major breakthrough in artificial intelligence with a high potential for predictive applications. https://www.bigdataspain.org/2017/talk/a-deep-learning-use-case-for-water-end-use-detection Big Data Spain 2017 November 16th - 17th Kinépolis Madrid

2. A DEEP
LEARNIN
G
USE
CASE
FOR WATER
END USE
DETECTION

3. Motivatio
n

4. Motivation
Urban water supply
• We need good demand management policies
to achieve a good sustainable development.
• Adding a new water source imply:
• Higher costs.
• Environmental damage.
• Poorer quality.
• “the largest, least expensive, and most
environmentally sound source of water […] is
the water currently being wasted in every
sector of our economy”.[1]
[1] Gleick, P. et al. (2003). Waste Not, Want Not: The Potential for
Urban Water Conservation

5. Motivation
End uses of water
• Residential use of water -> The 70% of
total water consumption.
• A good understanding of the demand and
its characterization could be very useful to
create good management policies.
• Several problems can be addressed using
AI techniques:
• Final use classification (dishwasher,
toilet, irrigation, taps).
• Water demand forecasting.

6. Motivation
The problem
• Installing a meter on each water device is
very expensive and intrusive.
• To overcome this problem, it is possible to
install a unique precision meter at the
home main water connection.
• Predictive models can read these meters
and make predictions:
• End use: Classification problem.
• Forecasting: Regression problem.

7. Motivation
Data Source
• Canal de Isabel II monitors since 2008 a
sample of 300 homes spread over the
region of Madrid.
• 15 million hours monitored for 9 years.
• 35 million of events.
• The sample is stratified and spread along
different geographical areas of the region
to be considered representative of the
domestic users of Madrid.
• The goal is the study of patterns of
consumption and end uses of urban water.

8. Motivation
Project information
7
PROJECT
TITLE
Pattern Recognition in Residential End Uses of Water
RESEARCH
LINE
Assurance of the balance (availability / demand)
CLIENT Canal de Isabel II
CONSORTIU
M
Exeleria: Preprocessing tasks
Treelogic: Machine Learning tasks
GOAL Developing an automatic system for identifying the
end uses of water in the domestic applications, from
the signals registered by water meters, using
advanced techniques of machine learning, such as
artificial neural networks (ANN) or other statistical
methods

9. Starting
Point

10. Starting Point
Hardware Infraestructure
9
WATER METER DATALOGGER

11. Starting Point
• Data was labeled by operators (experts)
who classify water use events using
specialized software.
• This task involves a considerable amount
of man-hours.
• 1 hour of an operator to analyse a two-
week period of data from each installation.

12. Starting Point
8 type of events
SHOWERS
(INCLUDING BATHTUBS)
DISHWASHERWASHING MACHINECISTERNS
LEAKS
FAUCETS
POOL IRRIGATION

13. Previous
analysis and
visualization

14. Previous analysis and visualization
Pulse to Flow
1
DATE COUNTER
(Number of accumulated pulses)
01/06/2008 0:47:35 31542
01/06/2008 0:48:13 31543
01/06/2008 0:48:55 31544
01/06/2008 0:49:38 31545
01/06/2008 1:20:29 31546
01/06/2008 1:20:46 31547
01/06/2008 1:21:03 31548
01/06/2008 1:21:20 31549
………………… …………………
BASELINE INFORMATION
• Date
• Number of pulse

15. Previous analysis and visualization
Pulse to Flow
1

16. Previous analysis and visualization
Pulse to Flow
1

17. Previous analysis and visualization
Pulse to Flow
1

18. Previous analysis and visualization
Pulse to Flow
1

19. Previous analysis and
visualization
Episodes
• An episode is a period of time where the
flow is distinct to zero and is between two
zero-flow instants.
• An episode may consist of one or more
events.
• An event only belongs to an episode.

20. Previous analysis and
visualization
Events
• An event is an elementary unit of
consumption that occurs in a period of time
of enough duration, in which the instant flow
can be clearly differentiated from the rest.
• A particular domestic use may consist of
one or more events.
• One or several events that converge in time
form an episode.

21. 3 domestic uses which involve 4 events
FAUCETS
1 EVENT
CISTERNS
1 EVENT
WASHING MACHINE
CYCLE 1
WASHING MACHINE
CYCLE 2
2 EVENT
Q
T

22. 3 domestic uses which involve 4 events and 3
episodes
Q
T
EPISODE 1 EPISODE 2 EPISODE 3
FAUCETS
1 EVENT
CISTERNS
1 EVENT
WASHING MACHINE
CYCLE 1
WASHING MACHINE
CYCLE 2

23. Previous analysis and
visualization
Events identification
• When an episode consist of more than one
event, the events are overlapped.
• Graphically the events are "stacked" on
others as a ladder.
• How do we discriminate events?
o It is the same event if…
⁻ The flow rate keeps constant or the
change is not significant.
o It is a different event if…
⁻ There is a significant change in the
flow rate.

26. Approach

27. Approach
Feature Extraction
2
37 FEATURES WERE EXTRACTED FROM EVERY EVENT: duration, volume, maximum flow,
initial Gradient, …

28. Approach
Deep Neural Networks
• Deep Learning (DL) is a major
breakthrough in artificial intelligence with a
high potential for predictive applications.
• It has been recognized as one of ten
breakthrough technologies according to
MIT Technology Review.
• DL has gone from being considered an
academic field to being applied in
engineering thanks to frameworks like
TensorFlow or CNTK.
• Very powerful, they can solve very complex
tasks.
• They require a large amount of data.
• Large training times, they require
specialized hardware for complex tasks.
• Slow classifiers.

29. Approach
Deep Neural Networks

30. Approach
Speedup (SDAs)
• A disadvantage of the backpropagation
algorithm is that the training fast in the last
layers (near the output), but very slow if
we are far away from the output.
• If we don’t have a lot of training data to
perform a high number of back propagation
iterations, we only train the layers at the
output..
• If we can initialize the neural network with
useful weights in the firsts layers, the
training procedure will speed up.
• If that initialization is not supervised we
can use unlabeled data.

31. Approach
Speedup (SDAs)
• Imagine a neural network that has one hidden layer
• With the same number of neurons in the input than in the output.
• We add noise to the input and we train the network to recover the original input.
• The network will learn to generalize because it will receive different data with the same output.
• The network will learn to identify useful features of the image.

32. Approach
Speedup (SDAs)
• How can I initialize an MLP using autoencoders?
• Stacking them.
• We can remove the decoding layer and attach another autoencder in the output.
• An autoencoder can just find basic useful weights.
• The idea of autoencder in Deep Learning is using several autoencers training in a sequential way
using the hidden layer as an input of the next autoencoder.

33. Speedup (SDAs)

34. Results
Benchmark
ACCURACY OF
DEEP NEURAL
NETWORKS
81.78%
In 1l meters
91.19%
In 0,1l meters
ACCURACY OF
SVMs
67.41%
In 1l meters
84.78%
In 0,1l meters

35. Results
Accuracy comparison of Deep Neural Networks and SVMs in every water use

36. What else can
Deep Learning
do for
water supply
companies…?

37. What else…?
Time Series
• Water supply companies are also interested on:
• Water demand forecasting.
• Weather or quantitative precipitation forecast:
o Volume of water in reservoirs.
o Alert systems.
• Time series forecasting.

38. What else…?
RNN
3
• Traditional NN assume that inputs are independents of each other.
• RNN incorporate memory that contains the essence of what has happened previously.

39. What else…?
LSTM
3
• A variant of RNN, capable of learning long term dependences.
• Internal architecture more complex than Simple RNN architecture.
• Most widely used type of RNN.

40. [**] https://datamarket.com/data/set/22ls/monthly-precipitation-mm-southwestern-mountain-region-1932-1966
Southwestern precipitation forecast
Monthly precipitation (mm.)
Southwester mountain region (1932-1966)
DATA SET
420
ROWS
2
COLUMNS
(number of month, precipitation)

41. Solution
• LSTM network
o Input – 20 timesteps, 1 feature
o Hidden Layer 1 – 20 LSTM
o Output – 1 neuron
• MSE -> 16,94
4

42. Monthly precipitation (mm.) · Prediction (last 63
months)

43. CONCLUSIO
NS
01 02
03 04
Data science can help us to
UNDERSTAND of the water
demand and its
characterization.
Deep Learning Models can
achieve very good results in
terms of ACCURACY when is
trained using large enough
datasets.
This METHODOLOGY is
actually in use for processing
data from the Panel for
residential consumption
patterns assessment and end-
uses monitoring project of
Canal de Isabel II in Madrid.
It could be very USEFUL to
create good management
policies.

44. THANKS
!
Roberto
Díaz
LEADER OF THE
DATA SCIENCE
RESEARCH
José
Antonio
Sánchez
SENIOR R&D
ENGINEER
THANKS
!

45. Contacto
Parque Tecnológico de
Asturias
Parcela 30
E33428 Llanera
Asturias
ESPAÑA
Avda. Manoteras, 38
Oficina D614
E28050 Madrid
ESPAÑA
T +34 902 286 386
central@treelogic.com
www.Treelogic.com