spark for behavioral analytics research: spark summit east talk by john w u

Spark for Behavior Analysis Research Ling Jin, Sam Borgeson, Anna Spurlock, Annika ToddDoris Lee, Alex Sim, John Wu

Lawrence Berkeley National Lab

www.lbl.gov

Adam Smith’s Most Famous Books?

Behavioral Analysis Research 3

One-minute Behavioral Economics

Simpler to analyze and predict Harder to predict -- Need massive data and computers

Behavioral Analysis Research 4

How Are Blackjack and Electric Power Grid Similar?Bust when demand is larger than supply Bust when over 21

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• Residential electricity records: 100,000 households, hourly electricity usage

• A region where electricity usage peaks in the summer time

• Randomized controlled trial of new rate structures -households are randomly placed in different groups• Control: using existing fixed rate for electricity• Active: households opted in to Time-of-Use Pricing• Passive: households defaulted in to Time-of-Use Pricing

• Data collected hourly over three years, one pre-rate, two after (labeled T-1, T, T+1)

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Reducing Peak Demands Through Pricing

Behavioral Analysis Research

Goal Method Policy ImplicationBetter baseline models of energy use

Gradient tree boosting Better program evaluation

Define relevant household characteristics:

Classify and segment households using easily accessible data

• Define representative load shapes

Adaptive K-means clustering

• Estimate household-specific cooling change points (AC set point)

Piecewise linear regression, bootstrapping

• Characterize customers into “Lifestyle Groups”

Blend behavioral theory with machine learning techniques

• Define relevant household energy characteristics

Simple feature algorithms (e.g.,mean, min, max, peak usage; variance; entropy, etc.)

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Research Examples

Behavioral Analysis Research

Baselines are Critical for Measuring Changes

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Current Standard: randomized control group

Predictions from new Baseline TechniqueActive group Passive group

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Group PT PT +1 MT −PT MT+1 −PT+1

Control 1.960 1.957 0.069 -0.020Passive 1.849 1.897 -0.027 -0.080Active 1.860 1.903 -0.164 -0.164

Observation: the Active group reduced their uses of electricity consistently over the two yeas of study during the peak-demand hours

P: predictedM: measured

Behavioral Analysis Research

Goal Method Policy ImplicationBetter baseline models of energy use

Gradient tree boosting Better program evaluation

Define relevant household characteristics:

Classify and segment households using easily accessible data

• Define representative load shapes

Adaptive K-means clustering

• Estimate household-specific cooling change points (AC set point)

Piecewise linear regression, bootstrapping

• Characterize customers into “Lifestyle Groups”

Blend behavioral theory with machine learning techniques

• Define relevant household energy characteristics

Simple feature algorithms (e.g.,mean, min, max, peak usage; variance; entropy, etc.)

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Research Examples

Behavioral Analysis Research

Daily Load Shape: DefinitionObjective / Definition• 24-hour electricity usage

pattern• To capture hour variations:

don’t care about constant usage

• Want to use load shape to study mechanism that might affect electricity usage, therefore concentrate on discretionary demand

Samples

10/26/2016

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• A load shape is the pattern created by 24 hours of demand data.

• Shapes are produced by patterns of occupancy, equipment ownership, and behavior

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Defining Load Shapes

Flat/unoccupied Solar Morn/work/eve

Out for lunch? AC dominated?

Active at night?

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Clustering approach

Normalization(load shapes)

Hourly smart meter data

(“customer-day”)

De-min

Dictionaryload shapes

Truncation

Adaptive K-means

Hierarchical Merging

Suitable parameters: • Error threshold• Violation rate30M customer-days

99 representative clusters/shapes

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Top 12 Clusters

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Cluster coverage

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Clustering Process

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Load data preprocessing

Load shape clustering

Post clustering processing

Smart meter customer-day load shape data

(24 hourly observations per customer-day)

Subsample of 100,000

customer-day load shapes

De-min*

Normalize

Eliminate load shapes with missing data or extremely low power

demand (below 0.2kW)

Adaptive k-means algorithm

(parameter θ chosen)

Hierarchical merging

Iterative truncation algorithm*

(parameter V chosen)

Definition of final dictionary of load shapes

Assign full dataset of customer daily load shapes to the closest dictionary load shape

* Innovations we introduce

Components of Clustering Process• Data cleaning and normalization• Remove households with very low demand (<0.2kW)• Normalization: compute hourly usage to hourly contribution to daily usage• Clustering• Centroid-based methods: Kmeans, Adaptive Kmeans• Hierarchical clustering: distance metric, linkage• Density-based clustering: DBSCAN• Model-based clustering: GMM• Judging cluster quality• Compactness: MIA, VRSE• Distinctness: SMI• Combined: DBI, Silhouette

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Clustering Quality Index

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Clustering Quality

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More compactMore distinct

Puzzling??

Best choice?

Kmeans Produces More Balanced Clusters

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Sizes of clusters as fractions of total number of samples

Behavioral Analysis Research

SummaryØ Examined a good number of clustering methods for identifying

daily usage profilesØ Short observation:– Centroid-based method (Kmeans) works the best on this set of dataØ Longer observations:– There are too many choices and no (really) clear winner– Centroid-based methods produce more compact clusters– Centroid-based methods produce more balanced cluster– DBSCAN declare many (up to 90%) data points as background– Our observation is different from previous published results (Chicco

2012)Ø Future work– Maybe a different clustering quality metric would work better?– Should examine alternative profile generation methods, other than

clustering Behavioral Analysis Research 17

Thank You.John WuEmail: John.Wu@nersc.gov