SIM Home Home of the Future

Transcription

1 California Plug Load Research Center Workshop SIM Home Home of the Future November 9, 2016 Linyi Xia California Plug Load Research Center California Institute for Telecommunications and Information Technology

2 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

3 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.

4 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 Entry Point

5 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* Entry Point * Items are out of the scope of the SCE sponsored project

6 Construction: Sustainability Reuse and Recycle Design and construct with reusable materials. Explore energy saving potentials: Device power management settings

7 SIM Home Design

8 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

9 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

10 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

11 Testing and Verification What we are NOT testing: 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

12 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.

13 TV: Testing Methodology: Case Study 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.

14 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

15 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 kWHr/year in error) To demonstrate opportunities of load shedding among plug load devices.

16 Plug Load Energy Simulation Approach

17 Simulation Sample Dataset: 50% use level Devices Def. Use level: 50% Power active idle sleep off active idle sleep off Thermo-pot Kitchen 28 min/t, twice a day(15min+30min) Rice cooker Kitchen 35min/t, twice a day Coffee maker Kitchen 10min/t, twice a day Macbook pro Office imac Office Speaker (0.51) Office Chargers (3) Office Laser MFD Office Plug-in lamps Bedroom LED TV Living Sound bar Living Blue ray DVD Living Wi-Fi router Bedroom Game console Living STB Living

18 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%, kwhr Schedule and power input impact linearly on TEC

19 SIM Home System Design SimHome Network System Diagram Device End Backend Services Applications Interfaces User/Frontend Home Entertainment NI DAQ Amazon AWS Amazon Echo Voice Feedbacks Home Office Wireless S Switches LabView Machine/PC of the SImHome Kitchen Sim Bulletin SimHome Server Insteon Hub C Router IP Security Camera Insteon Backend/API EMMA User s Phone/ EMMA/ SimBulletin Sensors Beacons

20 Web App: SimBulletin Overview

21 Web App: SimBulletin Data Visualization

22 Web App: SimBulletin Energy Budget

23 Web App: SimBulletin Control

24 EMMA: Energy Monitor Management Assistant

25 EMMA: Energy Monitor Management Assistant

26 Thank you!