SIM Home – Home of the FutureNovember 9, 2016

California Plug Load Research Center Workshop

Linyi XiaCalifornia Plug Load Research Center

California Institute for Telecommunications and Information Technology

Challenges in Residential Plug Loads Efficiency

• Aggregated Energy: Devices are becoming more efficient, but the number of devices are increasing

• Complex and fast evolving landscape:• Diverse user groups in ownership and preferences• IOT edge devices• Age of the devices• Home of the future

• Actionable information (Decision science) for decision makers at all levels:• End users• Policy makers• Incentives

Typical Approaches

• Modeling:• No widely accepted tools (for plug load appliances)• No widely accepted models• Normalized profiles are nonexistent.

• Testing:• Power consumption are intensively examined, but not energy• Difficulties in energy consumption estimation and measurement

• Incentives and Policies.

Goals of SimHome Project• Demonstrate a relevant “home of the future”:

– Sustainability– Interactive home management– Advanced metering technology– Home automation and control– Energy dashboard reporting

• Gap analysis on:– Simulation profiles– Testing procedures– Energy management strategies

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SIM Home Proposed Approaches

• Literature researches• Setup and implementation of

IOT enabled devices• Aggregated testing by

appliance groups• User experience design to

better convey the energy related information*

• Machine learning data analytics*

• Information for decision makers via multiple media platforms*

• Home level simulation*

* Items are out of the scope of the SCE sponsored project

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Construction: Sustainability• Reuse and Recycle

– Design and construct with reusable materials.

• Explore energy saving potentials:– Device power management

settings

SIM Home Design

User Experience Design• Accomplishments

– EMMA: Energy Monitor & Management Assistant

– Location based notification

– EMMA speech functions– Web App:

– Data visualization– Customized device

control– Data analytics for fault

detection– Energy budgets and

machine learning for over drafting protection

– APIs for smart home device integration

– Amazon Echo

• On going researches– Augmented reality to

promote energy awareness for:

– Decision makers– Energy efficiency

enthusiastic– Tips and estimations of home

energy efficiency upgrades

– Study of effectiveness of information delivery medias

Device Ownership and Use Profile Research

• Datasets:– RASS: Residential Appliance Saturation Survey 2003, 2009– RECS: Residential Energy Consumption Survey 2009– CLASS: The California Lighting and Appliances Saturation

Survey 2012– SKA: The Small Kitchen Appliances Study 2015– PASUS: The portable Appliances Saturation and Usage Study

Device List

• Home Office:– Desktop computer– Laptop computer

(and charger)– Multifunction inkjet

device– Computer speakers– Charger for mobile

device– 3D printer

• Home Entertainment:– Television, LCD– HD cable and/or HD

satellite box– Blu-ray or HD DVD

player– Video game console

• Kitchen*:– Automated drip

coffeemaker – Pod coffee maker – Thermo-pot

Testing and Verification

• What we are NOTtesting:– Features and

performance– Power consumption

by operation mode– Durability and

robustness– Comparison with

similar products– And more…….

• What we are testing:– Variation in device

groups– Boundary conditions

for device use time durations

– Power management*– NOT the effectiveness,

but impacts of user alteration of such settings

– IOT solutions

Testing Methodology Gap Analysis

• Devices without energy efficiency regulations:– Coffee maker (up to 1350W)– Thermal pot (working nonstop)– 3D printer– Rice cookers/Slow cookers*– *International standards available

• Devices may benefit from energy efficiency in addition to power efficiency regulations:– Device undergo distinctive operation modes to get

the “job” done (e.g. dish washer) – Device constantly draws energy (e.g. IOT devices)– Device with minimal incremental energy when

varying load size.

Testing Methodology: Case Study• TV:

– Testing procedures are hard to find, qualification standards are not user friendly

– Tested value far exceeds claimed value (close to 3x). – Inaccurate energy labeling– Some power management settings do not appear to

be effective*– Testing sequences are not realistic use cases.– Standardized testing videos do not have sounds

– *Auto off is disabled by default; TV will not transition into screen saver mode when input media enters idle loop; screen saver mode does not save energy.

Test Setup• 14 channels:

– 7 High accuracy– 7 Medium accuracy

• LabView with:– cDAQ chassis– NI 9227, 9242, 9246– USB DAQ 6003 (Async)– MySQL DB

• Data collected:– Active, reactive power– Power factor– THD for both V and I

Plug Load Energy Simulation Gap Analysis– To capture the range in

energy consumption for a limited pool of plug loads with varying use profiles (up to 3.36x differences)

– To explore possible error ranges of traditional energy simulation (up to 3180.55kWHr/year in error)

– To demonstrate opportunities of load shedding among plug load devices.

Plug Load Energy Simulation Approach

Simulation Sample Dataset: 50% use levelDevices Def.

Use level: 50% Power

active idle sleep off active idle sleep off

Thermo-pot Kitchen 28 min/t, twice a day(15min+30min) 644.8 0.37

Rice cooker Kitchen 35min/t, twice a day 262 0.77

Coffee maker Kitchen 10min/t, twice a day 784.25

Macbook pro Office 4 5 1 14 13 1

iMac Office 5 10 2 7 80 63 0.8

Speaker (0.51) Office 5 10 2 7 3.95 3.4 1.3

Chargers (3) Office 24 1.28

Laser MFD Office 2 10 12 445 55 1.5

Plug-in lamps Bedroom 3 12 9 9.97 0.4 0

LED TV Living 4 1 19 154 38 0.133

Sound bar Living 4 1 19 60 30 2

Blue ray DVD Living 5 19 4.78 0.18

Wi-Fi router Bedroom 24 6.16 0

Game console Living 3 4 2 15 140 90 10 0.5

STB Living 5 19 3 1

Simulation Sample Results

10% use level 50% use level 90% use level

• Choosing 50% mode will overestimate 10% case up to 3.36x• Choosing 50% mode will underestimate 90% case by 61%, 3180.55

kWHr• Schedule and power input impact linearly on TEC

SIM Home System DesignSimHome Network System Diagram

User/FrontendDevice End Backend Services Applications Interfaces

SimHome Server

Insteon Hub

Insteon Backend/API

LabView Machine/PC of the SImHome

RouterUser’s Phone/ EMMA/  SimBulletin

C

IP Security Camera

Amazon AWS

Sim Bulletin

EMMA

Amazon Echo Voice Feedbacks

BeaconsSensors

SWireless Switches

Home Entertainment

Home Office

NI DAQ

Kitchen

Web App: SimBulletin Overview

Web App: SimBulletin Data Visualization

Web App: SimBulletin Energy Budget

Web App: SimBulletin Control

EMMA: Energy Monitor Management Assistant

EMMA: Energy Monitor Management Assistant

Thank you!