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Background

This technology proposes an automated control strategy for Variable Air Volume (VAV) systems in buildings using Wireless Sensor Networks (WSN) and Automated Fault Detection and Diagnosis (AFDD). By optimizing damper positions and modulating fan speed, this strategy can save energy while ensuring comfort.

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Background

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Presentation Transcript

  1. Background Source: Cisco • Typical Building Lifecycle (Cisco, 2005) • More than 75% of Expenses occur in the Operating Period • But most of the time and focus are in the development period • Building energy systems do not operate as developed • 15~30% of energy is wasted (Katipamula and Brambley, 2005) • Up to 70% of the service costs can be saved (Li and Braun, 2007) • Smart Bldg solutions: Automated monitoring, diagnostics and optimization • Wireless sensor network based monitoring is one of the enablers

  2. Example Technology: Cascade VAV Optimal Control Strategy Using WSN and AFDD + + - - Terminal boxes: 1, 2,…i,…n VFD Controller Fan Duct work Static Pressure Controller Wireless Sensor Network DP/Load analyzer Automated Fault Diag. Damper position of the critical zone • Optimal VAV control strategy is to maintain the damper associated with the critical zone fully open • Primary control loop: Reset the static pressure set point (SPSP) • Secondary control loop: Modulate fan speed to meet the SPSP • WSN: obtain all damper positions -- AFDD: Diagnosis on system functionality • DP/Load analyzer: Identify the critical zone

  3. Preliminary Results Comfort can not be met from 8~10 due to deficiency of supply air if either airflow rate reset or OA temperature reset method is used The optimal VAV control strategy (damper position rest) using WSN can save more energy than the other two existing methods while ensuring comfort

  4. Testing Bed Development

  5. Establishment of the UNL Smart Building Center Indus Sponsors Target: $128,517 Underground: $10k $upport Technol Transfer UNL Smart Building Center Sensus M.I. Yr1: $56,640 Yr2: $60,000 NCERS Yr1:$62,946 Yr2:$65,254 AE Intelligent Bldg. Syst. Lab CEEN WSN Lab Fac . Mem: 3 & Postdoc: 1 Ph.D. grad: 5 & MS Grad: 3 $upport

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