Buildings & Energy ! Buildings are major energy consumers 76% of - PowerPoint PPT Presentation

E MPIRICAL C HARACTERIZATION AND M ODELING OF E LECTRICAL L OADS IN S MART H OMES Sean Barker , Sandeep Kalra, David Irwin, and Prashant Shenoy University of Massachusetts Amherst Department of Computer Science

Buildings & Energy ! Buildings are major energy consumers • 76% of US electricity, 48% of energy ! Potential for “smart” buildings • Reduce energy usage, increase efficiency • Demand-side energy management (DSEM) Sean Barker (sbarker@cs.umass.edu) 2

Smart Buildings ! Need energy data for effective DSEM ! Lots of data from smart meters • Ongoing deployments by utilities ! Use data to optimize energy use • E.g., peak load reduction Peak Usage Off-Peak Shiftable Load Sean Barker (sbarker@cs.umass.edu) 3

Optimization Challenges ! Optimization requires understanding energy use ! Sense-analyze-control for smart buildings • Smart meters provide sensing • Modeling key to analysis and control ! Model and predict building energy usage • Both aggregate and individual loads ! Build models of electrical loads in homes Sean Barker (sbarker@cs.umass.edu) 4

Prior Work: Simple Models ! Model loads as ‘on-off’ devices • Load is either on (with fixed power) or off • E.g., a light bulb light 250 Power (W) 200 150 100 50 0 0 1 2 3 4 Time (min) ! Simple extension: multiple discrete ‘on’ states • e.g., [REDD] Sean Barker (sbarker@cs.umass.edu) 5

Modeling Challenges ! Problem: Modern devices exhibit complex usage washing machine ! Not easily described by ‘on-off’ models ! Need better models to capture these behaviors ! Goal: Better load models for complex devices Sean Barker (sbarker@cs.umass.edu) 6

Outline ! Motivation ! Features of Electrical Loads ! Modeling Household Devices ! Applications of Models ! Conclusions Sean Barker (sbarker@cs.umass.edu) 7

Features of Electrical Loads ! Approach: empirically characterize individual loads to distill common characteristics ! Data: 100+ devices, 2+ years of data • 1 sec data resolution ! Divide loads into classes based on their electrical usage properties Sean Barker (sbarker@cs.umass.edu) 8

Resistive Loads ! Voltage and current waveforms aligned ! Devices with heating elements • Lights • Toaster • Coffeepot • Oven • Space heater Sean Barker (sbarker@cs.umass.edu) 9

Resistive Load Features 1000 coffee maker light 250 800 Power (W) Power (W) 200 coffee 600 150 light maker 400 100 200 50 0 0 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 Time (min) Time (min) 1600 toaster 1400 Power (W) 1200 1000 toaster 800 600 400 200 0 0 1 2 3 4 Time (min) Sean Barker (sbarker@cs.umass.edu) 10

Resistive Load Features 1000 coffee maker light 250 800 Power (W) Power (W) 200 coffee 600 150 light maker 400 100 200 50 0 0 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 Time (min) Time (min) 1600 1000 toaster coffee maker zoom 1400 980 Power (W) 1200 1000 960 toaster 800 940 600 400 920 200 900 0 0 1 2 3 4 880 Time (min) Sean Barker (sbarker@cs.umass.edu) 10

Resistive Load Features 1000 coffee maker light 250 800 Power (W) Power (W) 200 coffee 600 150 light maker 400 100 200 50 0 0 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 Time (min) Time (min) 1600 toaster 1470 toaster zoom 1400 Power (W) 1200 1460 1000 1450 toaster 800 600 1440 400 200 1430 0 0 1 2 3 4 1420 Time (min) ! On-off (small loads), on-off with decay (large loads) Sean Barker (sbarker@cs.umass.edu) 10

Inductive Loads ! Current waveform lags voltage ! Devices with AC motors • Refrigerator/freezer compressor • Air conditioner • Vacuum Sean Barker (sbarker@cs.umass.edu) 11

Inductive Load Features 1600 2500 vacuum cleaner Central A/C 1400 2000 Power (W) Power (W) 1200 1000 1500 800 vacuum A/C 1000 600 400 500 200 0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Time (sec) Time (min) 800 450 refrigerator freezer 400 700 350 Power (W) Power (W) 600 fridge freezer 300 500 250 400 200 300 150 200 100 100 50 0 0 0 20 40 60 80 100 120 0 5 10 15 20 25 30 35 40 45 Time (min) Time (min) Sean Barker (sbarker@cs.umass.edu) 12

Inductive Load Features 1600 2500 vacuum cleaner Central A/C 1400 2000 Power (W) Power (W) 1200 1000 1500 800 vacuum A/C 1000 600 400 500 200 0 0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Time (sec) Time (min) 800 450 refrigerator freezer 400 700 350 Power (W) Power (W) 600 fridge freezer 300 500 250 400 200 300 150 200 100 100 50 0 0 0 20 40 60 80 100 120 0 5 10 15 20 25 30 35 40 45 Time (min) Time (min) ! AC motor surge current, then stabilization Sean Barker (sbarker@cs.umass.edu) 12

Non-Linear Loads ! Non-sinusoidal current draw ! Electronic devices • Switch-mode power supplies • Computers • Televisions ! Fluorescent lights ! Battery chargers Sean Barker (sbarker@cs.umass.edu) 13

Non-Linear Load Features 180 LCD TV 45 Mac Mini Mac Mini 160 40 140 Power (W) 35 Power (W) 120 30 100 25 80 20 60 LCD TV 15 40 10 20 5 0 0 0 1 2 3 4 5 0 5 10 15 20 Time (min) Time (min) 1600 microwave heat HRV 1000 1400 recovery Power (W) 1200 Power (W) 800 ventilator 1000 (HRV) 600 800 microwave 600 400 400 200 200 0 0 0 1 2 3 4 5 0 5 10 15 20 Time (min) Time (min) Sean Barker (sbarker@cs.umass.edu) 14

Non-Linear Load Features 180 LCD TV 45 Mac Mini Mac Mini 160 40 140 Power (W) 35 Power (W) 120 30 100 25 80 20 60 LCD TV 15 40 10 20 5 0 0 0 1 2 3 4 5 0 5 10 15 20 Time (min) Time (min) 1600 microwave heat HRV 1000 1400 recovery Power (W) 1200 Power (W) 800 ventilator 1500 fluctuation zoom 1000 1480 (HRV) 600 1460 800 1440 600 400 1420 400 1400 200 200 1380 0 0 0 1 2 3 4 5 0 5 10 15 20 Time (min) Time (min) ! Unpredictable variations (often from a stable level) Sean Barker (sbarker@cs.umass.edu) 14

Buildings & Energy ! Buildings are major energy consumers 76% of - PowerPoint PPT Presentation

E MPIRICAL C HARACTERIZATION AND M ODELING OF E LECTRICAL L OADS IN S MART H OMES Sean Barker , Sandeep Kalra, David Irwin, and Prashant Shenoy University of Massachusetts Amherst Department of Computer Science Buildings & Energy ! Buildings

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