A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | |
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1 | Year | Paper | Application | Summary | ||||||||||||||||||||
2 | Buildsys | 2009 | The Case for Apportionment | energy apportionment | get occupancy from card swiping, and then correlate with whole building energy usage | |||||||||||||||||||
3 | Buildsys | 2009 | The Energy Dashboard: Improving the Visibility of Energy Consumption at a Campus-Wide Scale | Energy dashboard | medium-granularity data visualization ( e.g Plug load, Machine-room, lighting, etc ) | |||||||||||||||||||
4 | Buildsys | 2009 | Evaluation of Energy-Efficiency in Lighting Systems using Sensor Networks | Evaluate lighting energy wastage using additional light sensors | Uses extra lighting sensors to see whether lighting energy is being wasted | |||||||||||||||||||
5 | Buildsys | 2009 | The Self-Programming Thermostat: Optimizing Setback Schedules based on Home Occupancy Patterns | Intelligent thremostat using occupancy data to reduce energy consumption | Uses motion sensors, reed switches on doors to measure occupancy. | |||||||||||||||||||
6 | Buildsys | 2009 | iSense: A Wireless Sensor Network Based Conference Room Management System-- | Meeting Room energy optimization using occupancy sensors | In 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 . | |||||||||||||||||||
7 | Buildsys | 2009 | Using Circuit-Level Power Measurements in Household Energy Management Systems. -::: | NILM - electrical appliances | clustering, max likelihood, histogram thinning | |||||||||||||||||||
8 | Buildsys | 2009 | Challenges in Resource Monitoring tx)nfor Residential Spaces | NILM - water and energy . overview | pattern matching over time series | |||||||||||||||||||
9 | Buildsys | 2009 | Energy Efficient Building Environment Control Strategies Using Real-time Occupancy Measurements | occupancy based energy reduction | using camera determine occupancy. Build occupancy models using multi-variate and gaussian models, and optimize heating/cooling system | |||||||||||||||||||
10 | Buildsys | 2009 | A Wireless Sensor Network Design Tool to Support Building Energy Management | |||||||||||||||||||||
11 | Buildsys | 2009 | Towards a Zero-Configuration Wireless Sensor Network Architecture for Smart Buildings | each sensor as web service - sMAP type | ||||||||||||||||||||
12 | Buildsys | 2009 | Efficient Application Integration in IP-Based Sensor Networks | REST transaction over low-power multi-hop wireless network. sensor nodes communicating through web services - RESTFUL interface | ||||||||||||||||||||
13 | Buildsys xd | 2010 | Wireless, Collaborative Virtual Sensors for Thermal Comfort | create additional virtual sensors | use analytical modeling to create virtual sensors where physical sensors don’t exist | |||||||||||||||||||
14 | Buildsys | 2010 | Using Simple Light Sensors to Achieve Smart Daylight Harvesting | daylight harvesting ( lower lighting energy using outside daylight ) | have sensors on windows which capture amount of light | |||||||||||||||||||
15 | E-Energy | 2010 | Managing End-User Preferences in the Smart Grid | Demand-response | Have utility function for each device. On a demand-response event, apply optimization function and utility functions to decide power level of devices | |||||||||||||||||||
16 | E-energy | 2010 | Policy-Driven Distributed and Collaborative Demand Response in Multi-Domain Commercial Buildings | Demand-response | electrical 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. | |||||||||||||||||||
17 | Buildsys | 2010 | HBCI: Human-Building-Computer Interaction | device power display | scan QR code on device , connect to cloud and get some display | |||||||||||||||||||
18 | Buildsys | 2010 | TinyEARS: Spying on House Appliances with Audio Sensor Nodes | energy apportionment | uses device acoustic signatures | |||||||||||||||||||
19 | Buildsys | 2010 | Granger Causality Analysis on IP Traffic and Circuit-Level Energy Monitoring | energy apportionment | zone- level power meters with network traffic to draw causal relationships | |||||||||||||||||||
20 | Buildsys | 2010 | NetBem: Business Equipment Energy Monitoring through Network Auditing | energy apportionment | network traces with power consumption graph | |||||||||||||||||||
21 | Buildsys | 2010 | Occupancy-Driven Energy Management for Smart Building Automation | HVAC efficiency using occupancy sensor | Occupany sensor built from PIR and reed door switch. Granularity of occupancy - whether or not the room was occupied. | |||||||||||||||||||
22 | Buildsys | 2010 | Occupancy Based Demand Response HVAC Control Strategy | HVAC efficiency using occupancy sensor | model occupancy using MCMC, run EnergyPlus simulations. occupancy detection using cameras. | |||||||||||||||||||
23 | Buildsys | 2010 | Contactless Sensing of Appliance State Transitions Through Variations in Electromagnetic Fields | NILM | using sensors which uses sensors to monitor changes in EMF to detect device state changes | |||||||||||||||||||
24 | Buildsys | 2010 | Private Memoirs of a Smart Meter | NILM : privacy risk using smart meter | look for signatures in high-resolution whole building power meter data | |||||||||||||||||||
25 | Buildsys | 2010 | Building-level Occupancy Data to Improve ARIMA-based Electricity Use Forecasts | occupancy based modeling | fine grained occupancy using PIR, CO2 and network logs. | |||||||||||||||||||
26 | Buildsys | 2010 | A Limited-Data Model Of Building Energy Consumption | |||||||||||||||||||||
27 | E-Energy | 2010 | Profiling Energy Use in Households and Office Spaces | local and remote storage of data collected from power meter sensors. Analysis on how much energy can be saved in each house | ||||||||||||||||||||
28 | Buildsys | 2011 | Towards an Understanding of Campus-Scale Power Consumption | Anomaly detection and occupancy modeling | Unsupervised 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 | |||||||||||||||||||
29 | Buildsys | 2011 | Managing Plug-Loads for Demand Response within Buildings | Demand-response, energy accounting | Use different information sources ( network, PIR ) and smart meters to implement demand response | |||||||||||||||||||
30 | Buildsys | 2011 | The Case for Efficient Renewable Energy Management in Smart Homes | distributed generation | control strategy exploration - when homes use energy only from grid, or grid + local solar | |||||||||||||||||||
31 | E-Energy | 2011 | EnergyPULSE: Tracking Sustainable Behavior in Office Environments | energy apportionment | PIR sensors for occupancy, track light (LDR sensor) and power usage (smart power meter) for each individual office | |||||||||||||||||||
32 | Buildsys | 2011 | COPOLAN: Non-Invasive Occupancy Profiling for Preliminary Assessment of HVAC Fixed Timing Strategies | occupancy based energy reduction | correlates power consumption and VLAN activity | |||||||||||||||||||
33 | Buildsys | 2011 | Enabling Building Energy Auditing Using Adapted Occupancy Models | occupancy modeling | Build occupancy model ( Gaussian, etc ) for one building and adapt parameters for another building with different floor plans | |||||||||||||||||||
34 | Buildsys | 2011 | A Living Laboratory Study in Personalized Automated Lighting Controls | Personalized lighting controls. save light energy | Make user interact with software which controls lighting. lights turn off unless user specifically asks them to be turned on | |||||||||||||||||||
35 | Buildsys | 2011 | WaterSense: Water Flow Disaggregation Using Motion Sensors | water fixture energy disaggregation | use motion sensors and water flow signatures to disaggregate water energy consumption | |||||||||||||||||||
36 | Buildsys | 2011 | Exploiting Home Automation Protocols for Load Monitoring in Smart Buildings | |||||||||||||||||||||
37 | Buildsys | 2012 | Creating a Room Connectivity Graph of a Building from Per-Room Sensor Unit | automated metadata : room connectivity | figure 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 | |||||||||||||||||||
38 | Buildsys | 2012 | Building the Case For Automated Building Energy Management | Energy display and automating some energy-saving behavior | ||||||||||||||||||||
39 | Buildsys | 2012 | Hot Water DJ: Saving Energy by Pre-mixing Hot Water | energy efficiency in water heating system | supply 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. | |||||||||||||||||||
40 | Buildsys | 2012 | Designing Cost-Efficient Wireless Sensor/Actuator Networks for Building Control Systems | energy efficient lighting using augemented sensor networks | optimize communication cost of sensor network. then apply model . shows savings in lighting energy | |||||||||||||||||||
41 | Buildsys | 2012 | Energy-Aware Meeting Scheduling Algorithms for Smart Buildings | energy efficient meeting room scheduling | small meetings held in smaller rooms, meetings more packed into same room, etc strategies. | |||||||||||||||||||
42 | Buildsys | 2012 | SensorAct: A Privacy and Security Aware Federated Middleware for Building Management | extensible system for storage of building data | file-system type abstraction for sensors / devices | |||||||||||||||||||
43 | Buildsys | 2012 | Active Actuator Fault Detection and Diagnostics in HVAC systems | Fault detection and diagnostics | Detect 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. | |||||||||||||||||||
44 | Buildsys | 2012 | Semi-Automated Modular Modeling of Buildings for Model Predictive Control | MPC | e 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. | |||||||||||||||||||
45 | Buildsys | 2012 | Toward Adaptive Comfort Management in Office Buildings Using Participatory Sensing for End User Driven Control | Participatory sensing for comfort feedback | cell-phone based participatory sensing. Users give HVAC preferences. No actuation. | |||||||||||||||||||
46 | Buildsys | 2012 | Thermovote: Participatory Sensing for Efficient Building HVAC Conditioning | Participatory sensing for comfort feedback and energy efficiency | cell-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. | |||||||||||||||||||
47 | Buildsys | 2012 | Building Application Stack (BAS) | portable building applications through driver abstraction | BAS 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 | |||||||||||||||||||
48 | E-Energy | 2012 | SmartCharge: cutting the electricity bill in smart homes with energy storage | smart charging using electric vehicles | an 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 | |||||||||||||||||||
49 | E-Energy | 2012 | nPlug: A Smart Plug for Alleviating Peak Loads | smart plugs | nPlug, 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 | |||||||||||||||||||
50 | Buildsys | 2012 | Accurate Real-Time Occupant Energy-Footprinting in Commercial Buildings | |||||||||||||||||||||
51 | Buildsys | 2013 | Exploiting Generalized Additive Models for Diagnosing Abnormal Energy Use in Buildings | Anomaly detection | Used models ( building sub-meter power data and time of day modeling ) to figure out anomalies | |||||||||||||||||||
52 | HomeSys | 2013 | Taking Smart Space Users Into the Development Loop | architecture for smart homes | compares 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. | |||||||||||||||||||
53 | Buildsys | 2013 | A Distributed Energy Monitoring and Analytics Platform and its Use Cases | data collection | uses custom hardware to collect data from dorms | |||||||||||||||||||
54 | Buildsys | 2013 | ZonePAC: Zonal Power Estimation and Control via HVAC Metering and Occupant Feedback | Data collection and website to capture human input | estimates 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 | |||||||||||||||||||
55 | HomeSys | 2013 | Towards user identification in the home from appliance usage patterns | energy apportionment | supervised 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 | |||||||||||||||||||
56 | Buildsys | 2013 | A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households | energy efficiency using intelligent USB temp sensors | USB 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 | |||||||||||||||||||
57 | Buildsys | 2013 | Reduce the Number of Sensors: Sensing Acoustic Emissions to Estimate Appliance Energy Usage | energy usage inference from acoustic signals | a 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 | |||||||||||||||||||
58 | HomeSys | 2013 | homeBLOX: Introducing Process-Driven Home Automation | home-human interaction | Ask 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 | |||||||||||||||||||
59 | Buildsys | 2013 | Circulo: Saving Energy with Just-In-Time Hot Water Recirculation | hot water energy efficiency | learn 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 | |||||||||||||||||||
60 | Buildsys | 2013 | Incentivizing Advanced Load Scheduling in Smart Homes | incetive mechanism to lower energy consumption | we 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 | |||||||||||||||||||
61 | Buildsys | 2013 | EnergyTrack : Sensor-Driven Energy Use Analysis System | integration of occupancy and other models to get energy efficiency | propose 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 | |||||||||||||||||||
62 | Buildsys | 2013 | Randomized Model Predictive Control for HVAC Systems | MPC | to save HVAC energy consumption. | |||||||||||||||||||
63 | 2013 | It’s Different: Insights into home energy consumption in India | NILM + local store data collection architecture | helps in dealing with power outages | ||||||||||||||||||||
64 | Buildsys | 2013 | Estimation of building occupancy levels through environmental signals deconvolution | occupancy modeling | Gives 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 | |||||||||||||||||||
65 | Buildsys | 2013 | Non-Intrusive Occupancy Monitoring using Smart Meters | occupancy modeling | 0 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 | |||||||||||||||||||
66 | Buildsys | 2013 | Occupancy Detection from Electricity Consumption Data | occupancy modeling | figures out whether home is occupied or not using high granularity overall energy meter | |||||||||||||||||||
67 | Buildsys | 2013 | ThermoSense: Occupancy Thermal Based Sensing for HVAC Control | occupancy modeling and energy efficiency based on that | occupancy modeling utilizes thermal based sensing and PIR sensors. Estimate number of people in room and optimize HVAC system that way. | |||||||||||||||||||
68 | HomeSys | 2013 | Human localization at home using kinect | occupancy/localization | localize using kinnect | |||||||||||||||||||
69 | Buildsys | 2013 | Online Learning for Personalized Room-Level Thermal Control: A Multi-Armed Bandit Framework | personal comfort in office spaces | automatically 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 | |||||||||||||||||||
70 | Buildsys | 2013 | Carrying My Environment with Me: A Participatory-sensing Approach to Enhance Thermal Comfort | personal comfort using participatory sensing | create 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. | |||||||||||||||||||
71 | Buildsys | 2013 | Optimal Personal Comfort Management Using SPOT+ | personal comfort, occupancy modeling, HVAC energy efficiency using predictive modeling of occupancy | SPOT+ 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 | |||||||||||||||||||
72 | E-Energy | 2013 | A cloud-based consumer-centric architecture for energy data analytics | privacy of sensor data in the cloud | Introduces 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 | |||||||||||||||||||
73 | HomeSys | 2013 | Living++: A Platform for Assisted Living Applications | smart home experiences | smart 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. | |||||||||||||||||||
74 | HomeSys | 2013 | The Smart Home Controller on Your Wrist | Smart Watch | building smart watch hardware | |||||||||||||||||||
75 | E-Energy | 2013 | Smart air-conditioning control by wireless sensors: an online optimization approach | |||||||||||||||||||||
76 | HomeSys | 2014 | Finding Roles for Interactive Furniture in Homes with EmotoCouch | couch expresses emotion | couch tells you when it is angry/happy/etc | |||||||||||||||||||
77 | Buildsys | 2014 | WattShare: Detailed Energy Apportionment in Shared Living Spaces within Commercial Buildings | energy apportionment | utilizes 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 | |||||||||||||||||||
78 | E-Energy | 2014 | EnergyLens: combining smartphones with electricity meter for accurate activity detection and user annotation | energy apportionment | combine 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 | |||||||||||||||||||
79 | HomeSys | 2014 | CARL: Activity-Aware Automation for Energy Efficiency | energy apportionment / activity detection | temperature, PIR , light , magnetic door sensors to detect activities ( supervised learning ) | |||||||||||||||||||
80 | E-Energy | 2014 | An occupant-participatory approach for thermal comfort enhancement and energy conservation in buildings | HVAC comfort improvement | To 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. | |||||||||||||||||||
81 | E-Energy | 2014 | Hot, Cold and In Between: Enabling Fine-Grained Environmental Control in Homes for Efficiency and Comfort | HVAC energy efficiency | Because 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 | |||||||||||||||||||
82 | HomeSys | 2014 | Placing information at home: Using room context in domestic design | Indoor localization + GUI | ||||||||||||||||||||
83 | HomeSys | 2014 | A user demand and preference profiling method for residential energy management | NILM + energy efficiency load scheduling | ||||||||||||||||||||
84 | HomeSys | 2014 | Smart heating control with occupancy prediction: How much can one save? | occupancy based energy reduction | develops models. predicts how much energy one can save. | |||||||||||||||||||
85 | Buildsys | 2014 | BlueSentinel: a first approach using iBeacon for an energy efficient occupancy detection system | occupancy detection | use Apple iBeacons | |||||||||||||||||||
86 | Buildsys | 2014 | PresenceSense: Zero-training Algorithm for Individual Presence Detection based on Power Monitoring | occupancy modeling | uses 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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