Smart An Open Data Set and Tools for Enabling Research in Sustainable Homes Sean Barker , Aditya Mishra, David Irwin, Emmanuel Cecchet, Prashant Shenoy, and Jeannie Albrecht† University of Massachusetts Amherst Williams College† Department of Computer Science
Smart Buildings for Sustainability ! 73% of U.S. grid power ! Efficiency, sustainability through smart homes ! Environmental benefits: • carbon footprint, renewables ! Economic benefits: • infrastructure, energy costs University of Massachusetts Amherst - Department of Computer Science 2
Challenges of Smart Home Design (a) no scheduling (b) with scheduling ! Algorithms , policies , ... peak = 3000W power power A/C 3 • Flattening demand A/C 2 peak = 1000W A/C A/C A/C A/C 1 1 2 3 • Shifting demand using one hour period one hour period stored energy (c) offline scheduling (d) online scheduling interactive loads • Optimizing renewables power power peak = 2000W interactive loads peak = 1000W A/C A/C A/C 1 2 3 one hour period one hour period ! Data collection • Building a collection testbed • Scaling the testbed • Maintaining the testbed Sean Barker (sbarker@cs.umass.edu) 3
The Conventional Approach ! “I need [X] data to try [Y]” • Build a customized sensing system • Temporarily deploy in a home • Collect planned data and move on ! Potential Drawbacks • Narrow data scope • Scalability of custom deployments • Verification may require broader data (e.g., NILM) Sean Barker (sbarker@cs.umass.edu) 4
The Smart* Approach ! Data collection in the Smart* project Smart • Breadth of data from many sources • Scalability using off-the-shelf components • Continuity of data collection over long periods ! Key Statistics • Deployments in three homes • 100+ distinct sensor streams • Energy usage, generation, weather, motion, doors, GPS... • House -level, circuit -level, device -level • Time granularities as fine as 1 second • Up to 2 years of history (and counting) Sean Barker (sbarker@cs.umass.edu) 5
The Smart* Open Data Sets ! Releasing two data sets today ! UMass Smart* Home Data Set • Significant subset of our home data ! UMass Smart* Microgrid Data Set • Energy dataset from 400+ homes ! Also some sensing utilities used in our infrastructure ! Goals: • Facilitating validation of techniques in sustainability • Identification of new research avenues in sustainable homes Sean Barker (sbarker@cs.umass.edu) 6
Outline ! Motivation and overview ! Smart* Open Data Sets ! Potential Uses and Applications ! Smart* Software Tools ! Summary Sean Barker (sbarker@cs.umass.edu) 7
Data Types: Aggregate Electrical Usage ! House-level data via commercial meter (e.g., eGauge, TED) • Real & apparent power, <1% error • One second granularity ! Voltage, frequency on both phases ! Circuit-level data via multiple current transducers (CTs) • Real & apparent, one second intervals • Many single-device circuits Sean Barker (sbarker@cs.umass.edu) 8
Data Types: Outlet-Level Electrical Usage ! Two types of outlet-level energy meters ! Insteon iMeter Solo • Powerline protocol, descendant of X10 ! Z-Wave Smart Energy Switch • Wireless protocol ! Almost all (>90%) loads monitored ! Scalability challenges within homes • E.g., low bandwidth, interference • [Hnat, SenSys 2011], [Irwin, BuildSys 2011] Sean Barker (sbarker@cs.umass.edu) 9
Accuracy of Sub-House Readings ! Multiple data granularities enable measurements of accuracy ! Current deployment: >99% of second-level circuit readings within 4% of aggregate • Much better than our previous meter 100 % Readings < Error 90 80 70 60 50 Grid vs. Σ Circuits 40 0 2 4 6 8 10 Error (%) Sean Barker (sbarker@cs.umass.edu) 10
Data Types: Electrical Generation ! Renewable deployment at one home • Three solar panels • Two wind turbines • Micro-inverters feed back into electric grid (net metering) ! Record current and attached battery voltage ! 5 second (average) data granularity Sean Barker (sbarker@cs.umass.edu) 11
Data Types: Switch Events ! Wall switch events provided by Insteon-enabled switches ! Drop-in replacements for mechanical wall switches 280 ! On/off/dim(%) events kitchen:lights:dim f(x)=(-13/5)x 240 Dim Level (%) • Switch energy use derived 200 160 from events and known 120 max wattages 80 • Provides another level of 40 energy redundancy 0 0 10 20 30 40 50 60 70 80 90 100 Wattage (W) Sean Barker (sbarker@cs.umass.edu) 12
Data Types: Other Events ! Motion data via Insteon (binary room-level readings) ! Door activity via Insteon (open/close) ! Heating activity via Insteon-enabled thermostats • Furnace on/off, temperature setpoint Sean Barker (sbarker@cs.umass.edu) 13
Data Types: Environmental Data ! Data from deployed weather stations • One minute granularity ! Indoor readings • Temperature & humidity • Rooms and appliances (e.g., fridge interior) ! Outdoor readings • Temperature & humidity • Rain and wind Sean Barker (sbarker@cs.umass.edu) 14
Microgrid Data Set ! 443 unique homes ! Per-home electrical usage data ! Single 24-hour period ! One minute granularity ! Homes located in US Sean Barker (sbarker@cs.umass.edu) 15
Outline ! Motivation and overview ! Smart* Open Data Sets ! Potential Uses and Applications ! Smart* Software Tools ! Summary Sean Barker (sbarker@cs.umass.edu) 16
Example Applications ! Cost Optimization: Energy storage to cut energy bills • [e-Energy 2012] ! Load Monitoring using home automation infrastructure • [BuildSys 2011] ! Renewable Prediction using weather forecasts • [SmartGridComm 2011] ! Demand Flattening: Load shifting to cut peak demand • [PerCom 2012] ! Privacy and commercial smart meters • [BuildSys 2010] • Closely related to Nonintrusive Load Monitoring (NILM) Sean Barker (sbarker@cs.umass.edu) 17
Demand Flattening: Load Shifting ! Many devices operate within a guardband range • Guardband provides ‘slack’ that can be used to timeshift ! E.g., power & environmental data reveals guardband 160 40 Power Temperature 140 39.5 Temperature (F) Power (watts) 120 39 100 80 38.5 60 38 40 37.5 20 0 37 Time (6 hours) Sean Barker (sbarker@cs.umass.edu) 18
NILM: Simultaneous Events ! Event collisions complicate disaggregation • What can we do to reduce them? ! Higher fidelity meters (smaller event ‘width’) 14000 Time Intervals (1 Hz) Coarser meters (7 second events) Finer meters (3 second events) 12000 10000 8000 6000 4000 2000 0 1 2 3 4 Events During Interval Sean Barker (sbarker@cs.umass.edu) 19
NILM: Simultaneous Events ! Event collisions complicate disaggregation • What can we do to reduce them? ! Higher fidelity meters (smaller event ‘width’) ! Dedicated device meters (remove devices from trace) 70000 Time Intervals (1 Hz) 0 dedicated meters (all devices) 1 dedicated meter 60000 3 dedicated meters 50000 40000 30000 20000 10000 0 1 2 3 4 Events During Interval Sean Barker (sbarker@cs.umass.edu) 19
Outline ! Motivation and overview ! Smart* Open Data Sets ! Potential Uses and Applications ! Smart* Software Tools ! Summary Sean Barker (sbarker@cs.umass.edu) 20
Sensing Software Tools ! ‘Off the shelf’ sensors are easy to use! • ...in theory, anyways ! Still have difficulties to deal with • Proprietary or difficult-to-script software • Immature open-source options • Not designed for continuous monitoring at scale ! Releasing utilities for Insteon and Z-Wave meters • Hides protocol details and simplifies configuration ! May release higher-level components of our sensing infrastructure in the future Sean Barker (sbarker@cs.umass.edu) 21
Summary ! Data sets with both breadth and depth are important for research in sustainability ! Releasing two data sets (and related utilities) today • UMass Smart* Home Data Set • UMass Smart* Microgrid Data Set • Periodic updates to come ! Go download them! http://smart.cs.umass.edu Questions? sbarker@cs.umass.edu Sean Barker (sbarker@cs.umass.edu) 22
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