BEST Lab Research: Intelligent Energy Efficiency for Commercial LightingCommercial LightingCommercial LightingIntelligent Decision-making with Smart Dust MotesEnergy Research ProjectsModeling the Decision SpaceModeling ResultsEmpirical Preference TestingEmpirical Testing - ResultsBenchmarkingBenchmarking ResultsBenchmarking ResultsCharacterization, Validation, and Fusion of Mote SignalsSomething About MoteSomething About MoteMote DataIlluminance Calibration –Mapping CurveIlluminance Calibration –Mapping CurveIlluminance Calibration –Mapping CurveIlluminance Calibration –Mapping CurveIlluminance Calibration –Mapping CurveTemperature Sensor Calibration –Mapping CurveTemperature Sensor Calibration –Mapping CurveTemperature Sensor Calibration –Mapping CurveSensor Fusion and Validation AlgorithmBEST Lab Research: Intelligent Energy Efficiency for Commercial LightingJessica GrandersonYao-Jung WenRebekah Yozell-Epstein Johnnie Kim Mary HaileCommercial Lighting• Electrical Consumption and Savings Potential • Advanced Commercial Control Technologies- Up to 45% energy savings possible with occupant and light sensors- Limited adoption in commercial building sectorCommercial Lighting• Problems With Advanced Control Technologies– Uncertainty is not considered --> sensor signals, estimation, target maintenance– Time is not considered, lost savings through demand reduction– All occupants are treated the same– Wires, retro-fit and commissioningIntelligent Decision-making with Smart Dust Motes• An intelligent decision algorithm allows:validation of sensor signalsuncertainty in illuminance estimationdifferences in preference and perceptionpeak load reduction/demand response• Smart dust motes potentially offer: wireless sensing at the work surface, increased sensing density, simpler retro-fitting and commissioning, wireless actuation, and an increased number of control pointsEnergy Research Projects• Characterization, validation, and fusion of mote signals• Modeling the decision space for automatic dimming in large commercial office spaces (cubicles)• Benchmarking a specific decision space for switching and occupancy patterns, proposed smart lighting design• Determination of occupant preferences and perceptions for a specific decision spaceModeling the Decision Space• Goal is a model that can balance occupant preferences and perceptions with real-time electricity prices in daylighting decisions•How?– Identify the relevant variables, their states and their interactions: for example, day and time (schedule) influence occupancy, which has two states, occupied and vacant– Devise a mathematical function based on the variables, that can place a value on each possible decision.Modeling ResultsEmpirical Preference Testing• Goal is to determine the illuminance ranges over which each occupant perceives the lighting to be too dark, too bright, or ideal•How?– Place a portable dimmable fluorescent light fixture at each occupant’s desk, and have the occupant adjust the lights up and down. Record the illuminance with a light meter for each dark ideal or bright point. Do this several times.Empirical Testing - Results• Paper-based tasks require more light than computer-based tasks• No single illuminance level was ideal for all of the occupants tested, for either task type• The bright range showed the most variability, while dark showed the leastBenchmarking• Goal is to determine the occupancy and switching patterns in a target space (BEST Lab) in order to estimate the energy savings potential offered by an intelligent lighting system•How?– Switching patterns were detected with the use of a photosensor and data logger – Occupancy patterns were determined by having each person in the space sign in and out of the lab for a period of several weeksBenchmarking ResultsAverage Conference Area Occupancy-0.500.511.522.533.50 5 10 15 20 25Time of Day (military time)Average OccupancyWednesdayThursdayFridaySaturdaySundayMondayTuesdayBenchmarking ResultsSwitching Patterns-20.00.020.040.060.080.0100.0120.00 5 10 15 20 25Time of Day (military time)Probability That Light Will Be OnMondayTuesdayWednesdayThursdayFridaySaturdaySundayCharacterization, Validation, and Fusion of Mote Signals• Purpose of characterization– Understanding what the sensed data reveal– Find possible failure mode• Purpose of validation– Noise rejection– Fault detection• Purpose of fusion– Provide reliable information of current environment for decision-making– Feed appropriate value back to the control systemSomething About Mote•Processorand Radio Platform– Atmega 128L processor (4MHz)– 916MHz transceiver– 100 feet maximum radio range– 40Kbits/sec data rateSomething About Mote•Sensor BoardMicrophonePanasonic WM-62AThermistorPanasonic ERT-J1VR103JLight SensorClairexCL9P4LMagnetometerHoneywellHmc1002AccelerometerAnalog DevicesADXL202JEBuzzerSiriusPS14T40A(missing)Mote DataDate & Time Sample No. Mote No. Channel Readings:::::03-Jun-2003 22:39:58 91 1 1 88603-Jun-2003 22:39:59 92 1 1 88403-Jun-2003 22:39:59 93 1 1 88403-Jun-2003 22:40:00 94 1 1 88503-Jun-2003 22:40:00 95 1 1 88103-Jun-2003 22:40:01 96 1 1 88303-Jun-2003 22:40:01 97 1 1 882::::::::::03-Jun-2003 22:40:58 207 1 1 88103-Jun-2003 22:40:58 208 1 1 88103-Jun-2003 22:40:59 209 1 1 88203-Jun-2003 22:41:00 210 1 1 88303-Jun-2003 22:41:00 211 1 1 88403-Jun-2003 22:41:01 212 1 1 880:::::0 200 400 600 800 1000 1200050010001500200025003000Sensor readings v.s. IlluminanceSensor readingsIlluminance (lux)0 200 400 600 800 1000 1200050010001500200025003000Sensor readings v.s. IlluminanceSensor readingsIlluminance (lux)Illuminance Calibration –Mapping Curve0 200 400 600 800 1000 1200050010001500200025003000Sensor readings v.s. IlluminanceSensor readingsIlluminance (lux)Illuminance Calibration –Mapping Curve0 200 400 600 800 1000 1200050010001500200025003000Sensor readings v.s. IlluminanceSensor readingsIlluminance (lux)Illuminance Calibration –Mapping Curve0 200 400 600 800 1000 1200050010001500200025003000Sensor readings v.s. IlluminanceSensor readingsIlluminance (lux)Illuminance Calibration –Mapping Curve0 200 400 600 800 1000 1200050010001500200025003000Sensor readings v.s. IlluminanceSensor readingsIlluminance (lux)Illuminance Calibration –Mapping Curve12 5 9 4 6 32.3962 10 2.9452 10 1.2208 10(0 780)yxxxx−−−=×−×+×≤<53 222.7671 10 5.3745 10 34.947 7512.4(780 940)yx