This document discusses using machine learning algorithms to model energy use. It describes how evolutionary algorithms can be used to model energy use in homes and offices, which can help identify problems and save energy. Specifically, it discusses using a meta-evolutionary algorithm that adapts the mutation rate over time based on what has worked best. This approach converges faster than traditional evolutionary algorithms. The document then discusses challenges with visualizing high-dimensional models and solutions for diagnostic visualization and video generation to help understand the machine learning process and solutions.

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Agenda
A sample problem
A general approach
Complications arise
Light is cast on the villains
Who flee from the scene

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Agenda Script
A sample problem
A general approach
Complications arise
Light is cast on the villains
Who flee from the scene

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Model Building in a Nutshell
Gather
data
Build
models
Predict
future
World
domination!
Fight fraud
Save the
planet
✔

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A Sample Problem

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Modeling Energy Use
• Modeling office and home energy use can save energy
• Guides retrofits
• Finds bad leaks
• Increases awareness and understanding of problems
• Demonstrated results of 20% or more savings
• Savings = less CO2 = less planet warming

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Modeling Energy Use
See ASHRAE RP-1050
http://bit.ly/1ovwGfy

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Modeling Energy Use (or not)

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Modeling Energy Use (complete hash)

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Some Notes on the Method
• Can’t change method since this is ASHRAE standard
• Small changes in cutoff can have ragged effect on model fit
– Linear methods out of the question
– Gradient based methods find local minima
• All parameters interact strongly
– Can’t solve for one at a time

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Evolutionary Algorithms
• Basic algorithm:
fill population with random solutions
do {
keep best x% of solutions
mutate survivors to fill population
} until happy with results
• Works great
• Converges very slowly
– If mutation is small, takes many, many steps to find best, gets trapped
– If mutation is too big, keeps jumping away from optimum

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Doesn’t work in practice

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Meta-Evolutionary Algorithms
• Meta mutation algorithm:
fill population with random solutions
do {
keep best x% of solutions
mutate survivors to fill population
use mutation size to set mutation rate per candidate
} until happy with results
• Works great
• Converges very fast
– If small jump works, we get more of that
– If big jump works, we get more of that

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Meta-Evolutionary Algorithms
• Meta mutation algorithm:
fill population with random solutions
do {
keep best x% of solutions
mutate survivors to fill population
use mutation size to set mutation rate per candidate
} until happy with results
• Works great
• Converges very fast
– If small jump works, we get more of that
– If big jump works, we get more of that

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Meta-Evolutionary Algorithms
• Algorithm may go wrong way
• May take wrong-size steps
• But it quickly learns to correct
• Bad strategies die out along with
bad solutions

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But There’s a Rub
• This new algorithm may be gang busters
– But it comes with new knobs to turn
• How can we tell where to turn them?
• How do we make sense of a seething mass of 5 dimensional
spiders?

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We need to look inside

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Demo Reel Synopsis
• Constant mutation rate failure example
• Meta-mutation succeeds
• Meta-mutation can handle highly correlated narrow valleys
• Very complex landscapes can be navigated
• Strategy shifts fluidly to find solutions

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Let’s put on a show!

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Not quite that simple
• Current problem is 5-dimensional
• Problem parameters don’t make sense directly
• So we need to show the human face of the problem
(that is where we started!)
• We also need dynamics to understand how the algorithm gets
where it goes

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Main-line Model and Visualization Flow
Data
repo
Solver
grep
Solver
JSON
model
d3 +
twistd
JSON
model
Conventional
Scalable

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How does R make video?

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Diagnostic Visualizations
Solver
JSON
model
Scalable
Logs
ScaleR ffmpeg

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Of Note
• RevoScaleR solves most of the parallelism issues
• We still want to run arbitrary R
• Some legacy functions are Particularly Unfriendly to hdfs
– png(filename) – requires conventional file access
– system(command) – assumes conventional file access
– ffmpeg (1) – assumes conventional file access

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Simple Solution
• MapR provides hdfs and NFS access to cluster
• All path names are the same
• Map reduce programs can use legacy POSIX code

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Diagnostic Videos
• 5D x 100 can get trapped in local minimum
– ’470 example
• 5D x 500 avoids trapping issues
– ’470 quiescence and resurgence
• 3D x 500 and 3D x 100 also avoid trapping
• Need to distinguish empty house from occupied
– ’771 shows poor fit to either regime, classic real world issue

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Lessons I Learned by Watching Movies
• Lower dimensional problems are easier
– Evolve baseline level and cut-points, solve for wing slopes
– Hybrid solutions are not “cheating”
• Real-world data always has surprises and I am always surprised
by this
• Can use 5P models as cluster “centroids” to handle 2-state
homes

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And there’s a
PRIZE in every
box!