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Copyright©2016,Oracleand/oritsaffiliates.Allrightsreserved.|Copyright©2016,Oracleand/oritsaffiliates.Allrightsreserved.|
DmytroMindraSeniorManager,ApplicaConsEngineeringUCliCesGlobalBusinessUnitOctober1,2016
Data Science for Energy Efficiency
Copyright©2016,Oracleand/oritsaffiliates.Allrightsreserved.|
Our Mission WeaimtoprovidetheindustrywiththemostcompletecloudplaLormfortheenCreuClityvaluechain,frommetertogridtoend-customers.
Network Meter-to-cash Endconsumer
Copyright©2016,Oracleand/oritsaffiliates.Allrightsreserved.|
over
50%of US residential
energy data
600+
billionmeter reads
nearly
2/3of US residential smart meter data
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Opower’scustomerengagementplaLormcombinesinsighLulanalyCcs,behavioralscience,andcuPng-edgeUXtohelpuCliCes
elevatethecustomerexperience
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• Estimate an unknown value
– Predict future usage
What is Machine Learning algorithms that solve a problem by learning from data
Copyright©2016,Oracleand/oritsaffiliates.Allrightsreserved.|
• Estimate an unknown value
– Predict future usage
– Estimate something about a home
What is Machine Learning
sqft
algorithms that solve a problem by learning from data
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• Estimate an unknown value
– Predict future usage
– Estimate something about a home • Find patterns in data
What is Machine Learning algorithms that solve a problem by learning from data
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• Want to estimate some value: – Does this household use GAS or ELECTRIC heat?
Standard machine learning setting
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• Want to estimate some value: – Does this household use GAS or ELECTRIC heat?
» Have something we know about each household that might help us estimate the unknown value
Standard machine learning setting
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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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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Estimating heat type
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Estimating heat type
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Estimating heat type
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• “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
Estimating heat type
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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”
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• Want to estimate some value: – Does this household use GAS or ELECTRIC heat?
» Have something we know about each household that might help us estimate
» Know the answer for some instances
Standard machine learning setting
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• Want to estimate some value: target variable » Have something we know about each household that might
help us estimate: features • Know the answer for some instances: labeled training set
Standard machine learning setting
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Goal: learn a function
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Goal: learn a function
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
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Goal: learn a 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
Coefficients quantify things like “relatively higher” Learned coefficients guaranteed to perform best on training set
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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%
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• 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
Standard machine learning setting
The function you use doesn’t really matter The function we used earlier was logistic regression
Others include SVM, nearest neighbor, neural networks
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• 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
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• Target variable: – Does household X have an EV? (classification)
• Use case: – Suggested rate changes, grid balancing
• Labeled training set: – EV rate code for some customers, other sources?
• Possible features: – Spikes in AMI data
Examples
Electric Vehicle Detection
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• Target variable: – Will customer X call customer service? (classification)
• Use case: – Proactive messaging, call volume reduction
• Labeled training set: – Call center data?
• Possible features: – High bills, income level?
Examples
Customer Service Call Propensity
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• Target variable: – Is household X a vacation home? (classification)
• Use case: – Better neighbor comparison, personalization
• Labeled training set: – Is this obtainable?
• Possible features: – Monthly or weekly usage patterns
Examples
Vacation home identification
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• Target variable: – What is customer X’s business type? (classification)
• Use case: – Utilities want to know this; so does our SMB team
• Labeled training set: – SMB has 3rd party labels
• Possible features: – Business name, AMI usage
Examples
Business type classification
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• Target variable: – What will usage be next week/month? (regression)
• Use case: – Bill forecast module; bill protect
• Labeled training set: – Past usage data
• Possible features: – Customer’s past usage, forecasted weather
Examples
Bill Forecasting
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• Target variable: – What would usage be for given weather? (regression)
• Use case: – Weather normalization module
• Labeled training set: – Past usage/weather data
• Possible features: – Customer’s past usage, weather
Examples
Weather Normalization
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• Everything we just saw was called “supervised learning” • What if we don’t have labeled data?
Unsupervised learning
Unsupervised Learning
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• 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
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• 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
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• 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
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• 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
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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
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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
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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
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Load Curve Archetypes
Steady Eddies
Daytimers
Night Owls
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3%
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4%
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6%
Hour of the day 0.00 4.00 8.00 12.00 16.00 20.00 24.00
3%
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4%
5%
6%
Hour of the day
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Evening Peakers
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Twin Peaks
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Hour of the day
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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.
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Improved Personalization
Help drive acceptance of neighbor comparison
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Improved Personalization
Recommendations tailored to profile type
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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
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Underneath the hood
Load shape
$
Monthly usage
Web behavior
Income
Home data
PredicCvemodel
• LiWparCcipaCon~20%• DecreasemarkeCngspendthroughincreasingrelevance
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EnergyDisaggregaConandSetpointEsCmaCon
Cooling
32%
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Jan Apr Jul Oct Jan Apr Jul Oct
Baseload
Heating Cooling
Energy Disaggregation
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Beyond Heating/Cooling Disaggregation Learn more about individual homes using just energy usage data (e.g., AMI, bills)
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Setpoint Detection
base load
cooling setpoint
one household
one hour
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Setpoint Detection
cooling setpoint - 88°
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Setpoint Detection
cooling setpoint - 76°
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Setpoint Detection
cooling setpoint - 64°
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Setpoint Detection
cooling setpoint - 79°
heating setpoint - 62°
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Setpoint Detection – Hourly Analysis
For any given temperature and hour of the day, what percentage of total usage is
due to cooling?
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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
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• 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