Wireless Sensor and Actuator Networks for Lighting Energy ...

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At the rate of 9.62 cents per kWh as used in the analysis in Chapter 9.5.1, the annual electricity cost for the intelligent control is $213. Compared to the original nondimmable control with $515 annual cost, use of the research system in a 15-occupant office generates $302 savings. The payback period for implementing the intelligent lighting system in this particular office is six years. The energy savings could be much greater, since worst-case and conservative assumptions were used in the calculation. Recall that all the requests for the maximum possible illuminance were defined to occur during the night. Moreover, it was assumed that the office was fully occupied during regular hours; more energy could be saved as the lighting for absent occupants would be discounted by the system. It should also be noted that these calculations and estimates do not take into consideration the savings due to the implementation of other lighting energy management strategies that could be integrated into the system, such as load shedding. 261
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Wireless Sensor and Actuator Networ
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Abstract Wireless Sensor and Actuat
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To my wife for having faith in me a
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Table of Contents Chapter 1 Introdu
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6.4.2 Lighting Optimization Algorit
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10.2.1 Theoretical Contributions...
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List of Figures Figure 1-1 Research
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Figure 7-8 Voltage divider. .......
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Figure 9-31 Energy savings vs. payb
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The integration of wireless sensor
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esulting lighting is optimal in the
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acquisition and wireless transmissi
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a central base computer for sensor
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optimal light settings were in turn
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possibility of wireless actuator ne
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Chapter 2 Motivation The motivation
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[21]. Lighting accounts for 30% of
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Table 2-1 summarizes each of the co
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from light level tuning impossible
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used in the progress of this resear
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are expected to autonomously config
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office, and industrial applications
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Chapter 3 Related Research and Lite
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Although radio frequency is a suppo
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channels with different capabilitie
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In practice, fusion of sensor data
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The emergence of wireless sensor ne
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A is empty f A (x) = 0, x X (3.1)
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Convex combination: the relation Th
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where no crisp boundary can be defi
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developed by Dantzig [87]. The ineq
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sensor network is far more valuable
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Figure 4-1 Mote-FVF algorithm archi
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Figure 4-2 Gaussian correlation cur
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predicted reading. Note that the of
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fuzzification and m for the defuzz
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mote sensors were arranged in a 3-b
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Figure 4-6 Mote-FVF with median val
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Figure 4-8 Comparison of variations
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The daylighting system application
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The key components are the predicti
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Figure 5-3 Prediction performance o
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Figure 5-5 Prediction performance o
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Among them, adaptive Wiener filteri
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even though the sensed environment
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5.3 Simulation and Experiment Resul
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Figure 5-11 Adaptive sensing with d
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Figure 5-13 Damped adaptive sensing
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Chapter 6 Optimal Lighting Actuatio
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of the workplane level illuminance
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office with K luminaires, for examp
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E sub = e pq e rs e xy 1
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optimizer for a consistency check.
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Figure 6-4 Pseudo code of the light
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simulation was to show that the opt
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It is obvious from Figure 6-6 and F
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Chapter 7 Wireless-Enabled Lighting
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determined to be 0-2000 lux. Conseq
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discrete outputs from the motes spa
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actuation technologies, and additio
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Figure 7-8 Voltage divider. Since t
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Given the 256-level dimming capabil
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Figure 7-11 Prototyping Luminaire S
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to its simplicity. Although coded w
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The second experiment was executed
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Chapter 8 System Verification on Hu
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commercial system was randomized fo
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8.2.1 Hardware Implementation in Sm
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eference point on the desktop was s
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commands were received. In addition
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were provided with three wireless p
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(a) Figure 8-6 Sensor placement of
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lower meter readings was that the m
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(2) Subtle and invisible shadows ma
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function keys are. The function key
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deliberately perturbed the lights d
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Table 8-2 Summary of the test on pr
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Current Level on the investigator
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Chapter 9 System Implementation and
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Figure 9-1 Floor plan of the small
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Figure 9-3 System operation diagram
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campus-wide lighting maintenance, w
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message is addressed. The destinati
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Both the actuation and the grouping
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The construction or updating of the
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determines if this message is for u
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Figure 9-12 Lighting preset setting
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Figure 9-13 Average hourly percent
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9.4 Daylight Response The second ph
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Furthermore, the structure allows t
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Figure 9-16 Daylight harvesting tes
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Figure 9-18 Sensor readings in the
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then gradually dimmed. After reachi
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Seven-occupant case with simulated
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under significant daylight. Again,
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newly calculated light settings may
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Annual energy consumption = ( Numbe
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Figure 9-30 Energy savings vs. payb
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have partially contributed to the h
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$40 per unit and the system achieve
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performance of the current research
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sensors. The research in [14] prese
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Other than optimizing the energy co
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lighting configuration in the offic
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communications. The adaptive sensin
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integration of the research system
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will broaden the application to dif
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system could be reallocated to oper
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entities could be tremendously crit
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[7] J. A. Veitch and G. R. Newsham,
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[20] K. A. Karmel, High Performance
Page 228 and 229: [34] P. Levis, S. Madden, J. Polast
Page 230 and 231: International Workshop on Wireless
Page 232 and 233: [57] LonMark International, "Fact S
Page 234 and 235: [71] S. Sahni and X. Xu, "Algorithm
Page 236 and 237: Proceedings of the 1st Internationa
Page 238 and 239: [99] SPOT: Sensor Placement + Optim
Page 240 and 241: [113] Advance Transformer Technical
Page 242 and 243: A.2 Circuit Schematics of Photosens
Page 244 and 245: A.4 Circuit Schematics of the Secon
Page 246 and 247: Appendix B Human Subject Test B.1 C
Page 248 and 249: (a) Sensor placement of subject No.
Page 250 and 251: B.2 Comparison of System Performanc
Page 252 and 253: B.3 Summary of the Responses from t
Page 254 and 255: B.4 Human Subject Test Approval Let
Page 256 and 257: B.5 Human Subject Test Protocol Nar
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Page 268 and 269: B.7 Human Subject Test Questionnair
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Page 272 and 273: B.8 Human Subject Test Interview Sc
Page 274 and 275: Luminaire surface depreciation: 1.0
Page 276 and 277: Table C-1 Daylight reception durati
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