Leveraging Big Data to Develop Next Generation Demand Side Management Programs and Energy Regulations

Daniel Young, Energy SolutionsMike McGaraghan, Energy SolutionsNate Dewart, Energy SolutionsPat Eilert, PG&EDan Hopper, SCE

Moneyball Analogy #1

“Some of the scouts still believed they could tell by the structure of a young man’s face not only his character but his future in pro ball.”-Michael Lewis. 2003. Moneyball.

“All I have is the box scores.” -Bill James, 1977. Baseball Abstract. Sabermetrics statistician.

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Conclusion: We can do better with more data, and the data we need is out there.

The Need for Data

The development of successful utility programs and energy codes and standards requires a LOT of data:

Base-case product performance

Tech options for higher efficiency/performance

Forecasts of future product performance trends

Incremental cost of improvement3

Example: Where Data Can Help

Image source: DOE study: Incorporating Experience Curves in Appliance Standards Analysis

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More Stringent

Mor

e Co

st E

ffecti

veHighest NPV?Highest NPV?

Less

Conserva

tive

Case Study: LED Lamps

• Goal: Ensure minimum performance across several operating parameters for LED lamps: • Light color, light quality, efficacy, lifetime,

dimmability, etc.• Opportunity: LEDs and big data • LED technology is rapidly improving, while

costs are rapidly decreasing• Several existing databases to track product

performance• Many existing industry forecasts to calibrate

against• Looking beyond efficiency

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Moneyball Analogy #2

“A hitter should be measured by his success in that which he is trying to do, and that which he is trying to do is create runs.” -Bill James.1979. Baseball Abstract. Sabermetrics statistician.

Conclusion: Focus on the right metrics and keep the end goal in mind.

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2012 Analysis

• Approach:• 700 unique price points were manually collected for over 500 unique

lamp models (not new, definitely not big data)• Multi-variable regression model to analyze the dataset (a little new) 7

ENERGY STAR?

CRI

CCT

Power Factor

Wattage

Efficacy

Light Output

Bulb Shape

Dimmability Lifetime

Price Modeling – 2012 Data

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Note: Results based on online retailer data, which we found to be significantly higher on average than in store prices.

Moneyball Analogy #3

“The power of statistical analysis depends on sample size…a right-handed hitter who has gone two for ten against left-handed pitching, cannot as reliably be predicted to hit .200 against lefties as a hitter who has gone 200 for 1,000.”-Michael Lewis. 2003. Moneyball.

Conclusion: We could use some more data.

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Next Step: Bringing in Big Data

• Retailer-based web crawler tool:• screen-scraping methods • retailer provided APIs (Application Programming

Interfaces) • Scope of data collection:• Nine online retailers• 3,000 unique price points• 1,000 unique LED lamp models• 50 different manufacturers• Data collected weekly 10

2012 Data vs 2014 Data

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Watts

Lamp Sh

ape / Typ

e

Lumens

Lumen M

aintenance

Color Tempre

rature

Efficacy

(lpw)

Dimmable (Y

/N)

Energy S

tar Qual. (

Y/N)

Warra

nty CRI

Power Facto

r

Beam Angle

Input Volta

ge R9

Color Consis

tency /

Change

Candlepower (intensit

y)

Product

Weight

Power Typ

e (AC/D

C)0

500

1000

1500

2000

2012 Data - Manually Collected 2014 Data - Web-Crawler

Lamp Property

# Pr

oduc

ts

Note: 2014 data is refreshed every week

Benefits of Big Data

• More data -> improvements to the regression analysis:• Individual models could be created for each lamp type• Additional independent variables analyzed• Comparable or improved explanatory power for each

model• New data is collected each week with minimal effort• Ability to monitor real-time performance and price

changes• Observe trends in performance and price

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Example Regression Results

Best fit model is based on:• Lumens • Brand• Energy Star Qualified

Metrics not independently impacting price include:• Dimmable• Color Temperature• CRI• Wattage• Beam Angle• Warranty Length• Diameter• Efficacy• Lumen Maintenance

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Observed Trends

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Implications on IMC

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No more inc cost for CRI?

Back to the Future• Key questions for IDSM program development and codes

and standards advocacy/evaluation:

What’s the baseline performance?

How do the best products perform?

How is performance changing over time?

What’s the incremental cost?

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Summary

Major Benefits

• Major increase in data volume and accuracy

• Better data for more effective programs and codes

• Saves significant time and resources over existing methods

Outstanding Issues

• How to use the data most effectively

• Linking to product performance databases

• Inconsistent retailer info and labeling

• Legality of web-crawling 17

Moneyball Analogy #4

“Statcast, a 3-D tracking system that provides detailed metrics on the locations and movements of the ball, the players, and even the umpires…will proliferate not just through the ranks of all professional sports but to youth sports, affecting everything from how games are taught to the statistical nomenclature of sport”-Billy Beane. July 7, 2014. “A Tech-Driven Revolution.” Wall Street Journal

Conclusion: The opportunities for big data have only just begun.

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