Smart Building Related Work
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YearPaperApplicationSummary
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Buildsys2009Challenges in Resource Monitoring for Residential SpacesNILM - water and energy . overviewpattern matching over time series
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Buildsys2009Using Circuit-Level Power Measurements in Household Energy Management SystemsNILM - electrical appliancesclustering, max likelihood, histogram thinning
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Buildsys2009The Case for Apportionmentenergy apportionmentget occupancy from card swiping, and then correlate with whole building energy usage
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Buildsys2009Energy Efficient Building Environment Control Strategies Using Real-time Occupancy Measurementsoccupancy based energy reductionusing camera determine occupancy. Build occupancy models using multi-variate and gaussian models, and optimize heating/cooling system
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Buildsys2009A Wireless Sensor Network Design Tool to Support Building Energy Management
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Buildsys2009Towards a Zero-Configuration Wireless Sensor Network Architecture for Smart Buildingseach sensor as web service - sMAP type
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Buildsys2009iSense: A Wireless Sensor Network Based Conference Room
Management System
Meeting Room energy optimization using occupancy sensorsIn addition to MS Outlook meeting software, use different sensors to pick up mic/light/PIR sensors to get occupancy and give feedback whether there are people in the meeting room or not .
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Buildsys2009Efficient Application Integration in IP-Based Sensor NetworksREST transaction over low-power multi-hop wireless network. sensor nodes communicating through web services - RESTFUL interface
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Buildsys2009The Energy Dashboard: Improving the Visibility of Energy
Consumption at a Campus-Wide Scale
Energy dashboardmedium-granularity data visualization ( e.g Plug load, Machine-room, lighting, etc )
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Buildsys2009Evaluation of Energy-Efficiency in Lighting Systems using
Sensor Networks
Evaluate lighting energy wastage using additional light sensorsUses extra lighting sensors to see whether lighting energy is being wasted
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Buildsys2009The Self-Programming Thermostat: Optimizing Setback
Schedules based on Home Occupancy Patterns
Intelligent thremostat using occupancy data to reduce energy consumptionUses motion sensors, reed switches on doors to measure occupancy.
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Buildsys2010Occupancy-Driven Energy Management
for Smart Building Automation
HVAC efficiency using occupancy sensorOccupany sensor built from PIR and reed door switch. Granularity of occupancy - whether or not the room was occupied.
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Buildsys2010Occupancy Based Demand Response HVAC Control StrategyHVAC efficiency using occupancy sensormodel occupancy using MCMC, run EnergyPlus simulations. occupancy detection using cameras.
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Buildsys2010Building-level Occupancy Data to Improve ARIMA-based
Electricity Use Forecasts
occupancy based modelingfine grained occupancy using PIR, CO2 and network logs.
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Buildsys2010Contactless Sensing of Appliance State Transitions
Through Variations in Electromagnetic Fields
NILMusing sensors which uses sensors to monitor changes in EMF to detect device state changes
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Buildsys2010TinyEARS: Spying on House Appliances with Audio Sensor
Nodes
energy apportionmentuses device acoustic signatures
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Buildsys2010Granger Causality Analysis on IP Traffic and Circuit-Level Energy
Monitoring
energy apportionmentzone- level power meters with network traffic to draw causal relationships
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Buildsys2010NetBem: Business Equipment Energy Monitoring through
Network Auditing
energy apportionmentnetwork traces with power consumption graph
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Buildsys2010HBCI: Human-Building-Computer Interactiondevice power displayscan QR code on device , connect to cloud and get some display
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Buildsys2010Private Memoirs of a Smart MeterNILM : privacy risk using smart meterlook for signatures in high-resolution whole building power meter data
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Buildsys2010Using Simple Light Sensors to Achieve Smart Daylight Harvestingdaylight harvesting ( lower lighting energy using outside daylight )have sensors on windows which capture amount of light
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Buildsys2010Wireless, Collaborative Virtual Sensors for Thermal Comfortcreate additional virtual sensorsuse analytical modeling to create virtual sensors where physical sensors don’t exist
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Buildsys2010A Limited-Data Model Of Building Energy Consumption
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Buildsys2011A Living Laboratory Study in Personalized Automated Lighting
Controls
Personalized lighting controls. save light energyMake user interact with software which controls lighting. lights turn off unless user specifically asks them to be turned on
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Buildsys2011Exploiting Home Automation Protocols for Load Monitoring in
Smart Buildings
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Buildsys2011Managing Plug-Loads for Demand Response within BuildingsDemand-response, energy accountingUse different information sources ( network, PIR ) and smart meters to implement demand response
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Buildsys2011WaterSense: Water Flow Disaggregation Using Motion Sensorswater fixture energy disaggregationuse motion sensors and water flow signatures to disaggregate water energy consumption
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Buildsys2011COPOLAN: Non-Invasive Occupancy Profiling for Preliminary
Assessment of HVAC Fixed Timing Strategies
occupancy based energy reductioncorrelates power consumption and VLAN activity
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Buildsys2011Enabling Building Energy Auditing Using Adapted Occupancy
Models
occupancy modelingBuild occupancy model ( Gaussian, etc ) for one building and adapt parameters for another building with different floor plans
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Buildsys2011The Case for Efficient Renewable Energy Management in Smart
Homes
distributed generationcontrol strategy exploration - when homes use energy only from grid, or grid + local solar
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Buildsys2011Towards an Understanding of Campus-Scale Power ConsumptionAnomaly detection and occupancy modelingUnsupervised Clustering different day's power usage using their frequency components. occupancy model includes classifier using HMM ( from network logs ) , time of day, day of week, etc
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Buildsys2012Toward Adaptive Comfort Management in Office Buildings Using
Participatory Sensing for End User Driven Control
Participatory sensing for comfort feedbackcell-phone based participatory sensing. Users give HVAC preferences. No actuation.
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Buildsys2012Thermovote: Participatory Sensing for
Efficient Building HVAC Conditioning
Participatory sensing for comfort feedback and energy efficiencycell-phone based app which tells you whether a user a hot ,cold, etc. Use that feedback in a control strategy to optimize temperature. Actuation done. results on real deployment.
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Buildsys2012Building the Case For Automated Building Energy ManagementEnergy display and automating some energy-saving behavior
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Buildsys2012Building Application Stack (BAS)portable building applications through driver abstractionBAS provides a fuzzy query interface allowing application authors to describe the building components they require in terms of functional and spatial relationships. The resulting queries implicitly handle multiple building designs. BAS also incorporates a hierarchical driver model, exposing common functions of building components through standard interfaces
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Buildsys2012SensorAct: A Privacy and Security Aware Federated Middleware
for Building Management
extensible system for storage of building datafile-system type abstraction for sensors / devices
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Buildsys2012Hot Water DJ: Saving Energy by Pre-mixing Hot Waterenergy efficiency in water heating systemsupply hot water on-demand and only hot enough for device. adds additional sensors to figure out find hot-water events, and temperature needed for those events.
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Buildsys2012Semi-Automated Modular Modeling of Buildings for
Model Predictive Control
MPCe standard geometry and construction
data to derive in an automated way a physical first-principles
based linear model of the building’s thermal dynamics. This
describes the evolution of room, wall, floor and ceiling temperatures
on a per zone level as a function of external heat
fluxes (e.g., solar gains, heating/cooling system heat fluxes
etc.). Second, we model the external heat fluxes as linear
functions of control inputs and predictable disturbances.
Third, we tune a limited number of physically meaningful
parameters. Finally, we use model reduction to derive a loworder
model that is suitable for MPC.
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Buildsys2012Active Actuator Fault Detection and Diagnostics in HVAC systemsFault detection and diagnosticsDetect stuck / malfunctioning actuators ( window closed / open etc ) by building a model, and perturbing the variables and seeing if the output matches up with the model.
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Buildsys2012Accurate Real-Time Occupant Energy-Footprinting in
Commercial Buildings
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Buildsys2012Designing Cost-Efficient Wireless Sensor/Actuator Networks for
Building Control Systems
energy efficient lighting using augemented sensor networksoptimize communication cost of sensor network. then apply model . shows savings in lighting energy
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Buildsys2012Energy-Aware Meeting Scheduling Algorithms
for Smart Buildings
energy efficient meeting room schedulingsmall meetings held in smaller rooms, meetings more packed into same room, etc strategies.
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Buildsys2012Creating a Room Connectivity Graph of a Building from Per-Room
Sensor Unit
automated metadata : room connectivityfigure out which rooms are connected using : spillover of artificial light
between rooms; occupancy detections due to movement between
rooms; and a fusion of the two
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Buildsys2013A Scalable Low-Cost Solution to Provide Personalised
Home Heating Advice to Households
energy efficiency using intelligent USB temp sensorsUSB temperature logger, placed on top of the thermostat,
in order to build a thermal model of the home and to infer the operational
settings of the heating system
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Buildsys2013Incentivizing Advanced Load Scheduling in Smart Homesincetive mechanism to lower energy consumptionwe argue that variable rate pricing plans do not incentivize consumers to adopt advanced load scheduling algorithms. proposes flat-power pricing, which directly incentivizes consumers to flatten
their own demand profile
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Buildsys2013It’s Different: Insights into home energy consumption in IndiaNILM + local store data collection architecturehelps in dealing with power outages
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Buildsys2013A Distributed Energy Monitoring and Analytics Platform
and its Use Cases
data collectionuses custom hardware to collect data from dorms
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Buildsys2013EnergyTrack : Sensor-Driven Energy Use Analysis Systemintegration of occupancy and other models to get energy efficiencypropose an analysis model for energy usage that
jointly considers occupancy levels and the utility provided by end-loads. Our occupancy estimation algorithm uses
PIR and CO2 sensors, and has a lightweight training requirement
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Buildsys2013Estimation of building occupancy levels through environmental
signals deconvolution
occupancy modelingGives exact number of occupants. occupancy estimation problem is formulated as a regularized deconvolution problem, where the estimated occupancy is the input that, when injected into the identified model, best explains the currently measured CO2 levels
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Buildsys2013Non-Intrusive Occupancy Monitoring using Smart Metersoccupancy modeling0 or 1 occupancy. observe that a home’s pattern of electricity usage generally changes when occupants
are present due to their interact with electrical loads. empirically evaluate these interactions by monitoring ground truth occupancy in two homes, then correlating it with changes in statistical
metrics of smart meter data, such as power’s mean and variance, over short intervals. In particular, we use each metric’s maximum value at night as a proxy for its maximum value in an unoccupied home, and then signal occupancy whenever the daytime value exceeds it
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Buildsys2013Occupancy Detection from Electricity Consumption Dataoccupancy modelingfigures out whether home is occupied or not using high granularity overall energy meter
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Buildsys2013ThermoSense: Occupancy Thermal
Based Sensing for HVAC Control
occupancy modeling and energy efficiency based on thatoccupancy modeling utilizes thermal based sensing and PIR sensors. Estimate number of people in room and optimize HVAC system that way.
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Buildsys2013Reduce the Number of Sensors: Sensing Acoustic Emissions to
Estimate Appliance Energy Usage
energy usage inference from acoustic signalsa system that
allows to identify the energy demand incurred by a user’s
action based on audio recordings using smartphones. More
precisely, we capture the user’s ambient sounds and applying
suitable filtering steps in order to determine the user’s
current activity. Our results indicate that our system is capable
of detecting 16 typical household activities at an accuracy
of 92%. By annotating the detectable household activities
with information about typical energy consumptions,
extracted from 950 real-world power consumption traces, a
good estimate of the energy intensity of the users’ lifestyles
can be made
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Buildsys2013Circulo: Saving Energy with Just-In-Time Hot Water
Recirculation
hot water energy efficiencylearn patterns of hot water usage in the home and to circulate hot water only when future hot water usage is highly likely, and not whole day
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Buildsys2013Exploiting Generalized Additive Models for Diagnosing Abnormal
Energy Use in Buildings
Anomaly detectionUsed models ( building sub-meter power data and time of day modeling ) to figure out anomalies
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Buildsys2013ZonePAC: Zonal Power Estimation and Control via HVAC
Metering and Occupant Feedback
Data collection and website to capture human inputestimates the heating, cooling and electrical power
consumption of each zone in a Variable Air Volume (VAV) type system using existing infrastructure sensors installed as part of the Building Management System (BMS). We provide the estimated zone power consumption as feedback to the occupants of the building over the web and on mobile devices along with other thermal comfort related measurements such as temperature and setpoin
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Buildsys2013Randomized Model Predictive Control for HVAC SystemsMPCto save HVAC energy consumption.
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Buildsys2013Online Learning for Personalized Room-Level Thermal
Control: A Multi-Armed Bandit Framework
personal comfort in office spacesautomatically learning the optimal
thermal control in a room in order to maximize the
expected average satisfaction among occupants providing
stochastic feedback on their comfort through a participatory
sensing application
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Buildsys2013Carrying My Environment with Me: A Participatory-sensing
Approach to Enhance Thermal Comfort
personal comfort using participatory sensingcreate model for user using his vote, and create profile. every room he goes to, carry his profile with him ( through phone ) . optimize thermal conditions in that zone based on his profile.
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Buildsys2013Optimal Personal Comfort Management Using SPOT+personal comfort, occupancy modeling, HVAC energy efficiency using predictive modeling of occupancySPOT+ system performs predictive control. Specifically, SPOT+ uses the knearest-neighbour algorithm to predict room occupancy and learning-based model predictive control (LBMPC) to predict future room temperature and to compute the optimal sequence of control inputs. T
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Buildsys2014PresenceSense: Zero-training Algorithm for Individual Presence
Detection based on Power Monitoring
occupancy modelinguses ultrasonic sensors, acceleration sensors, wifi points and individual power monitoring data , trains semi-supervised learning and detects presense of particular user.
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Buildsys2014BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system
occupancy detectionuse Apple iBeacons
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Buildsys2014WattShare: Detailed Energy Apportionment in Shared Living
Spaces within Commercial Buildings
energy apportionmentutilizes signal strength values from WiFi scans
and audio signals from the microphone as input data sources from the smartphone, per phase power consumption from the 3–phase smart meter and some metadata that can be easily collected (e.g. type of appliances in each room and distribution of the three electrical phases across different rooms)
to achieve room level energy apportionment. We use WiFi signal strength to estimate the room occupancy while the audio data is used to differentiate between the events occurring across different rooms
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E-Energy2010Profiling Energy Use in Households and Office Spaceslocal and remote storage of data collected from power meter sensors. Analysis on how much energy can be saved in each house
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E-Energy2010Managing End-User Preferences in the Smart GridDemand-responseHave utility function for each device. On a demand-response event, apply optimization function and utility functions to decide power level of devices
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E-energy2010Policy-Driven Distributed and Collaborative Demand
Response in Multi-Domain Commercial Buildings
Demand-responseelectrical devices communicate among themselves to organize autonomously into appropriate organizational control groups (possibly hierarchical), and negotiate among themselves the most appropriate form of collective DR adaptation.
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E-Energy2011EnergyPULSE: Tracking Sustainable Behavior in Office
Environments
energy apportionmentPIR sensors for occupancy, track light (LDR sensor) and power usage (smart power meter) for each individual office
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E-Energy2012nPlug: A Smart Plug for Alleviating Peak Loadssmart plugsnPlug, a smart plug that sits between
the wall socket and deferrable loads such as water
heaters, washing machines, and electric vehicles. nPlugs combine real-time sensing and analytics to infer peak periods as well as supply-demand imbalance and reschedule attached appliances in a decentralized manner to alleviate peaks whenever possible
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E-Energy2012SmartCharge: cutting the electricity bill in smart homes with energy storagesmart charging using electric vehiclesan on-site battery array to store low-cost energy for use during high-cost periods. SmartCharge's algorithm reduces electricity costs by determining when to switch the home's power supply between the grid and the battery array. The algorithm leverages a prediction model we develop, which forecasts future demand using statistical machine learning techniques
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E-Energy2013A cloud-based consumer-centric architecture for energy data analyticsprivacy of sensor data in the cloudIntroduces notion of virtual home(Vhome). VHome is a virtualized execution environment hosted in a cloud-based server that provides three services: (a) storage for home energy use data, (b) an application runtime for executing applications that analyse this data, and (c) trusted web-based services for interaction with the gateway, other cloud-based services, and user devices (described in more detail below). A VHome is owned by the consumer and hosted by a VHome SaaS provider in an IaaS
cloud
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E-Energy2013Smart air-conditioning control by wireless sensors: an online optimization approach
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E-Energy2014EnergyLens: combining smartphones with electricity meter for accurate activity detection and user annotationenergy apportionmentcombine readily available sensor data (i.e. home level electricity meters and sensors on smartphones carried by the occupants) and metadata information (e.g. appliance power ratings and their location) for activity inference. Our proposed EnergyLens system intelligently fuses electricity meter data with sensors on commodity smartphones -- the Wifi radio and the microphone -- to infer, with high accuracy, which appliance is being used, when its being used, where its being used in the home, and who is using it
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E-Energy2014Hot, Cold and In Between: Enabling Fine-Grained
Environmental Control in Homes for Efficiency and
Comfort
HVAC energy efficiencyBecause they do not have fine-grained control for multiple rooms, residential HVAC systems often spend a lot of energy to condition unoccupied areas of the home. So they augment sensors (temp sensors, booster fans, space heaters, power strips, server-side control software ). By heating and cooling different rooms to different setpoints at different times, one can leverage one's understanding about the way spaces in the home are actually used in order to
fine-tune the environmental settings
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E-Energy2014An occupant-participatory approach for thermal comfort enhancement and energy conservation in buildingsHVAC comfort improvementTo minimize complaints, the current practice of the facility management is to adopt very conservative temperatures, leading to massive waste of energy. In this paper, we actively take thermal comfort into consideration. We propose a participatory approach allowing the occupants provide feedback regarding their comfort levels. A major challenge for a participatory design is to reduce intrusiveness of the system. To this end, we develop a temperature comfort correlation model that can build a profile for each occupant. The decision of setpoint temperature can be primarily model-driven, requiring minimal inputs of the occupants. Get occupant comfort input through cell phones.
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HomeSys2013The Smart Home Controller on Your WristSmart Watchbuilding smart watch hardware
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HomeSys2013homeBLOX: Introducing Process-Driven Home Automationhome-human interactionAsk user to specify process. e.g "Getting up". User draws a graph of what actions need to be done on what trigger. Then this system carried it out. Example : When Birgit’s alarm clock rings at 7am (process trigger),the bedside lamp and radio are turned on to help her wakeup. When Birgit gets up (pressure mat), the bathroom is prepared by turning the lights on. When she enters the bathroom (pressure mat), the playing music follows her, etc
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HomeSys2013Human localization at home using kinectoccupancy/localizationlocalize using kinnect
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HomeSys2013Living++: A Platform for Assisted
Living Applications
smart home experiencessmart home in a home with dementia patients. Applications built : temperature control, calendar, indoor localization using specialized hardware, activity and vital sign monitoring, log viewer, reminder, fall detection.
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HomeSys2013Towards user identification in the home from appliance usage patternsenergy apportionmentsupervised learning based approach for user identification from a dataset of appliance usage collected across five users and three kitchen appliances over a period of eight weeks
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HomeSys2013Taking Smart Space Users Into the
Development Loop
architecture for smart homescompares HomeOs, BOSS ( Berkeley ) and makes a case for (1) a repository
for interface definitions, (2) an App Store and an App
Manager, and (3) multi-dimensional ratings Does not describe any applications.
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HomeSys2014A user demand and preference profiling method for residential energy managementNILM + energy efficiency load scheduling
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HomeSys2014Placing information at home: Using
room context in domestic design
Indoor localization + GUI
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HomeSys2014Finding Roles for Interactive Furniture
in Homes with EmotoCouch
couch expresses emotioncouch tells you when it is angry/happy/etc
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HomeSys2014Smart heating control with
occupancy prediction: How much can one save?
occupancy based energy reductiondevelops models. predicts how much energy one can save.
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HomeSys2014CARL: Activity-Aware Automation
for Energy Efficiency
energy apportionment / activity detectiontemperature, PIR , light , magnetic door sensors to detect activities ( supervised learning )
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