Residential Non-Intrusive Load Monitoring Overview

Presented by Matt Smith, SDG&E, Emerging Technologies

E Source Forum 2014

September 29–October 2, 2014

How much energy does your refrigerator use?

Why does it matter?

Utilities • Refine customer rebate

and incentive programs • Qualify new products for

residential rebates • Improve relationships

with customers • Understand customer

behavior to improve capacity planning

• Identify/verify appliances that could participate in Demand Response

Customers • Understand your bill • Plan your monthly budget • Identify/repair/replace

energy “hogs” • Take advantage of rate

structures (TOU/Tiers) • Be able to make a

financial decision for when to use an appliance

How much energy can we really save? ACEEE analyzed results from 36 different studies between 1995-2010 and found energy savings varied based on the data given to customers

How do we get that data to residential customers?

Circuit/Plug Monitoring Smart Appliances

Home Area Network / Smart Meter

Electricity Bill Green Button Data

Can we use smart meter data more intelligently?

wattseeker.com

What companies are pursuing NILM?

Belkin Enetics

Smappee Detectent

Bidgley Onzo

Blue Line Innovations PlotWatt

Energy Aware Navetas

Intel EEme

? LoadIQ

Verdigris ?

What did SDG&E do?

• Outfitted 10 Homes • Monitored all of the circuits in the home to get true

energy-usage values • Collected 10-second meter data via an approved HAN

device

• Solicited Disaggregation Vendors • Provided data to the vendors at different frequencies

(10s, 1 minute, 15 minute, 1hr) • Assessed accuracy of their disaggregation

What did we see in our study?

• Disaggregation accuracy improves with increased input frequency (HAN data better than GB)

• Home survey data is useful but not required • Zigbee & Internet signals are critical • Large and regular loads (pool pumps and electric

vehicles) are the easiest to disaggregate

EV Disaggregation

What are the challenges we saw?

• Vendors don’t all detect the same end uses Vendor 1 Vendor 2 Vendor 3 Vendor 4

Fridge X X X Microwave X X EV X X X Pool Pumps X X “Pool” Oven X Laundry “Washer/Dryer” “Cooking/ Laundry” “Tumble Dryer” “Dryer” Dishwasher X Base Load “Background Loads” “Always On” ”Other” ”Base” HVAC X “Heating/Cooling” “HVAC/Water Heating” “AC” WH “Water Heating” “Water Heater” Other “Solar (experimental)” ”Lifestyle”, “Heat”

• Vendors choose to report energy usage on different time scales Vendor 1 Vendor 2 Vendor 3 Vendor 4

Minute X Hour X X Day X X X Month X X

Additional challenges

• Whole-building measurement frequency needs to be high to disaggregate the smaller appliances

• Higher-frequency data is more expensive • In addition to being more expensive, high-

frequency measurements are also more sensitive to connectivity issues

• We saw 92% connectivity with a HAN gateway connected to the meter reading the meter every 10 seconds. (i.e. 8 out of every 100 readings was missing)

What’s next for NILM?

• Large-scale field testing: 100s or 1,000s of homes • Include AC run-times from smart thermostat data • Test with sub-one-second active power, apparent

power, and voltage loggers • Evaluate actual energy savings after installing or

repairing equipment • Test disaggregation of gas appliances

Thank you! Contact: Matt Smith, SDG&E Emerging Technologies

Accuracy results table