Data Science for Energy Efficiency (Dmytro Mindra Technology Stream)

Copyright © 2016, Oracle and/or its aﬃliates. All rights reserved. | Copyright © 2016, Oracle and/or its aﬃliates. All rights reserved. |
Dmytro Mindra
Senior Manager, ApplicaCons Engineering
UCliCes Global Business Unit
October 1, 2016
Data Science
for
Energy Efficiency

Copyright © 2016, Oracle and/or its aﬃliates. All rights reserved. |
Our Mission
We aim to provide the industry with the most complete cloud plaLorm for the enCre
uClity value chain, from meter to grid to end-customers.
Network Meter-to-cash End consumer

over
50% 
of US residential
energy data
600+
billion 
meter reads
nearly 
2/3 
of US residential
smart meter data

Opower’s customer engagement plaLorm combines insighLul
analyCcs, behavioral science, and cuPng-edge UX to help uCliCes
elevate the customer experience

• Estimate an unknown value
– Predict future usage
What is Machine Learning
algorithms that solve a problem by learning from data

– Estimate something about a home
sqft

– Estimate something about a home
• Find patterns in data

• Want to estimate some value:
– Does this household use GAS or ELECTRIC heat?
Standard machine learning setting

»  Have something we know about each household that might
help us estimate the unknown value
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Estimating heat type
What do we know about a household that might help
us estimate whether it has gas or electric heat?

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kWh
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Therms

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Therms
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kWh

• “Features” that help us estimate heat type:
– Difference between winter gas usage and shoulder gas usage
– Ratio between winter gas usage and shoulder gas usage
– Difference between winter elec usage and shoulder elec usage
– Ratio between winter elec usage and shoulder elec usage
0
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10
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Therms
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kWh

Estimating heat type – suggestion from a client
We got a couple complaints from XECO customers
The client suggested some improvements…
“significant bump”
“relatively higher”
“significant increase”

help us estimate
»  Know the answer for some instances

• Want to estimate some value: target variable
help us estimate: features
• Know the answer for some instances: labeled training set

Training set

Goal: learn a function

0
400
800
1,200
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2,000
Jan
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input output

Training consists of learning parameters or
coefficients of our function
score = coeff1 * elec_diff +
coeff2 * elec_ratio +
coeff3 * gas_diff +
coeff4 * gas_ratio
function: if score > 0, then estimate is ELEC
otherwise estimate is GAS
positive
negative

score = coeff1 * elec_diff +
coeff2 * elec_ratio +
coeff3 * gas_diff +
coeff4 * gas_ratio
function: if score > 0, then estimate is ELEC
otherwise estimate is GAS
Coefficients quantify things like
“relatively higher”
Learned coefficients guaranteed to
perform best on training set

Standard machine learning pipeline
Training Set Evaluation Set Real Life
train the function evaluate how well
the function predicts
use the function on
new data to get our
answers
Ja
M
M
Jul
Se
No
coeff1: 1.38
coeff2: 0.25
coeff3: 3.59
coeff4: 2.84
Model accuracy: 86%
Baseline accuracy: 72%

• Want to estimate some value: target variable
– Can be category (ELEC/GAS) or number (e.g., kWh)
– Category – classification; number – regression
»  Have something we know about each instance that might
help us estimate: features
• Know the answer for some instances: labeled training set
The function you use doesn’t really matter
The function we used earlier was logistic regression
Others include SVM, nearest neighbor, neural networks

• Target variable:
– Will household X participate in program Y? (classification)
• Use case:
– Program targeting
• Labeled training set:
– We import past participation data from many clients
• Possible features:
– Combination of demographic and behavioral data
Examples
Program Propensity

– Does household X have an EV? (classification)
• Use case:
– Suggested rate changes, grid balancing
– EV rate code for some customers, other sources?
– Spikes in AMI data
Examples
Electric Vehicle Detection

– Will customer X call customer service? (classification)
• Use case:
– Proactive messaging, call volume reduction
– Call center data?
– High bills, income level?
Examples
Customer Service Call Propensity

– Is household X a vacation home? (classification)
• Use case:
– Better neighbor comparison, personalization
– Is this obtainable?
– Monthly or weekly usage patterns
Examples
Vacation home identification

– What is customer X’s business type? (classification)
• Use case:
– Utilities want to know this; so does our SMB team
– SMB has 3rd party labels
– Business name, AMI usage
Examples
Business type classification

– What will usage be next week/month? (regression)
• Use case:
– Bill forecast module; bill protect
– Past usage data
– Customer’s past usage, forecasted weather
Examples
Bill Forecasting

– What would usage be for given weather? (regression)
• Use case:
– Weather normalization module
– Past usage/weather data
– Customer’s past usage, weather
Examples
Weather Normalization

• Everything we just saw was called “supervised learning”
• What if we don’t have labeled data?
Unsupervised learning
Unsupervised Learning

• Unsupervised learning is looking for patterns in the data
• Don’t know the right answer, and there is no “right answer”
• E.g., clustering – how many clusters are there?
Unsupervised learning

Data Science at Opower
Mission:
Generate valuable, actionable insights for utility clients
and their customers and deliver them into our products
Methods:
Employ machine learning techniques to develop
customer models and behavioral understanding

Data Science workflow
Research
•  Data exploration
•  Accuracy testing
•  Prototyping
Initial Rollout
•  Professional Service
•  Pilot
General Availability
•  Productionalized as a service
•  Available to all clients
Research
•  Continued exploration
•  Accuracy testing

MLAB
Two goals:
(1) be a home for ongoing machine learning projects that are
not part of any scrum
(2) provide people an opportunity to be exposed to and learn
about Machine Learning by participating in side projects
Ongoing Projects:
Seasonal load curves, pool/washer/dryer detection,
churn prediction, electric vehicle detection

PersonalizaCon Through Load
Curve Analysis

Load Curves – All Customers

Load Curve Archetypes
Steady Eddies
Daytimers
Night Owls
0.00 4.00 8.00 12.00 16.00 20.00 24.00
3%
Proportionofusage
ineachhour
4%
5%
6%
Hour of the day
0.00 4.00 8.00 12.00 16.00 20.00 24.00
3%
Proportionofusage
ineachhour
4%
5%
6%
Hour of the day
0.004.00 8.00 12.00 16.00 20.00 24.00
3%
Proportionofusage
ineachhour
4%
5%
6%
Hour of the day
Evening Peakers
0.00 4.00 8.00 12.00 16.00 20.00 24.00
3%
Proportionofusage
ineachhour
4%
5%
6%
Hour of the day
Twin Peaks
0.00 4.00 8.00 12.00 16.00 20.00 24.00
3%
Proportionofusage
ineachhour
4%
5%
6%
Hour of the day

Segmentation

Targeted Messaging: Afternoon Peakers
This is an alert from UtilCo: Tomorrow,
Wednesday, July 10th is a peak day.
From 2 PM to 7 PM join UtilCo
customers by reducing your electric
use. Simple ways to save on peak
days include postponing dishwashing
and other large appliance use until the
peak day is over. Thank you for helping
us save! To opt out of phone alerts,
press 9.

Improved Personalization
Help drive acceptance of neighbor comparison

Improved Personalization
Recommendations tailored to profile type

Program Propensity

Target the right people with utility programs
Target likely participants
•  Some customers are more likely to
participate in any program
Target specific customers for
certain programs
•  Different types of customers are better
fitted for different utility programs,
indicated by their propensity
•  Target low propensity customers for
simple programs, and high propensity
customers for more involved customers
High Propensity Program
Low Propensity Program

Underneath the hood
Load shape
$
Monthly usage
Web behavior
Income
Home data
PredicCve
model
•  LiW parCcipaCon ~20%
•  Decrease markeCng spend
through increasing relevance

Energy DisaggregaCon and
Setpoint EsCmaCon
Cooling
32%

Jan Apr Jul Oct Jan Apr Jul Oct
Baseload
HeatingCooling
Energy Disaggregation

Disaggregation at Opower

Beyond Heating/Cooling Disaggregation
Learn more about individual homes using just energy usage data (e.g., AMI, bills)

Setpoint Detection
base load
cooling setpoint
one
household
one hour

Setpoint Detection
cooling setpoint - 88°

Setpoint Detection

Setpoint Detection
heating setpoint - 62°

Setpoint Detection – Hourly Analysis

Setpoint Detection – Hourly Analysis
For any given temperature and hour of
the day, what percentage of total usage is
due to cooling?

Setpoint Detection – hourly analysis

Accurate Disaggregation

Tip Targeting

Household Targeting For DR Event
Setpoint: 74°
Event savings: 3 kWh
DR: MAYBE
Setpoint: 79°
Event savings: 0.5 kWh
DR: NO
Setpoint: 68°
Event savings: 5.5 kWh
DR: YES

Bill Forecasting

• Identify machine learning problems
– If you ever say to yourself, I wish I knew X about a
customer, maybe we can!
– Come talk to the Data Science team
• Is your problem supervised or unsupervised?
– Do we have data with labeled target variables?
• We’re about to get more data
– What can we do with it?
– What new problems are there?
– What old problems can better be solved with new data?
Conclusions

Thanks!

Data Science for Energy Efficiency (Dmytro Mindra Technology Stream)

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