1 Using Webcrawlers to Estimate Incremental Measure Costs for the Retail Plug-Load Portfolio (RPP) Program November 21, 2014

Webcrawlers and RPP Agenda: Review of webcrawling as applied to LED lamps Applying webcrawlers to other new product categories Comparison of the webcrawling methodology to the CPUC’s IMC study 2

Applications of Webcrawling and Big Data to LED Lamps Project Goal: Understand relationship between price and performance for LED lamps. Study metrics: Light color, light quality, efficacy, lifetime, and dimmability. Research Implications: Identify changes in costs over time Look beyond efficiency toward product performance 3

2012 Analysis Conducted by Energy Solutions for PG&E Approach: 700 unique price points were manually collected for over 500 unique lamp models Multi-variable regression model to analyze the dataset 4 ENERGY STAR? CRI CCT Power Factor Wattage Efficacy Light Output Bulb Shape Dimmability Lifetime

Price Modeling – 2012 Data 5 Note: Results based on online retailer data, which we found to be significantly higher on average than in store prices.

2014 Analysis: Applying Big Data Retailer-based web crawler tool: Retailer provided APIs (Application Programming Interfaces) Screen-scraping methods Scope of data collection: 9 online retailers 3,000 unique price points 1,000 unique LED lamp models 50 different manufacturers Data collected weekly 6

A Difference in Magnitude: 2012 vs Data Collection 7 Note: 2014 data is refreshed every week

Some 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 8

Sample 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 9

Observed Trends 10

Implications for IMC over Time 11 No more IMC for CRI? Moving towards a more dynamic understanding of IMC and performance

Developing a Dynamic Understanding of Products IMC is one of many issues that can be informed by data analysis What’s the market’s baseline performance? How do the best products perform? How is performance changing over time? What’s the incremental cost? 12

Outstanding Questions for this Effort 13 Opportunities for Refinement Identifying new ways to use data most effectively Linking to product performance databases Inconsistent retailer info and labeling Developing improved links between in-store and online data Better understanding regional pricing impacts (rebates) Remaining Questions Legal issues of web-crawling

Applications of Webcrawlers and Big Data Agenda: Review of webcrawlers applied to LED lamps Applying webcrawlers to new product categories Comparison of the webcrawling methodology to the CPUC’s IMC study 14

Building on Prior Work Ongoing Parallel Efforts LED bulbs webcrawlers Assess compliances with CEC Appliance Standards Development of paper on scaling webcrawler effort Existing Infrastructure Established extraction mechanisms for 9 different online retailers (Best Buy, Costco, Lowe’s, Home Depot, Walmart, ACE Hardware) 15

Applying Past Experience to New Product Categories For New Products Straightforward for retailers in previous effort Identify key product features to track Perform initial data requests Refine process over time to improve data quality For New Retailers Requires significantly more time to understand retailer API, website layout. 16

Sample Product Features: Refrigerators Primary Product Features: Brand Model Configuration Defrost Type Through the Door Ice, Water Total Capacity (Freezer, Fresh) Energy Data Energy Star Qualified? Annual Electricity Use 17

Applications of Webcrawlers and Big Data Agenda: Review of webcrawlers applied to LED lamps Applying webcrawlers to new product categories Comparison of the webcrawling methodology to the CPUC’s IMC study 18

IMC Estimation: A Comparison of Methodologies Traditional IMC Studies: Given high cost and time to execute, restricted to high priority measures Managed by the CPUC Take 2-3 years to complete Conducted every 3-5 years Webcrawler Approach: Better suited to rapidly- changing markets Use a similar analytic approach (e.g., hedonic price models) Census of products on all available site New products can be added as needed Data collected faster and in higher volume Data must be adjusted for differences between brick-and- mortar and online price points 19

Next Steps Test approach on 1-2 products, expand to remaining products Timeline: May be between 1-2 months to develop IMC estimates for all product categories for current retailers. Additional time is required for additional retailers 20

Questions?