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Curbing the Energy Curve

2) Correct the Curve’s Shape
Having taken the energy baseline, the next step is to steady it.

“Using the data points, we wrote just six total rules that analyze the shape of the curve in the context of occupancy,” Quinn explains. “They identified low- or no-cost opportunities to correct poor start-up and shut-down sequences, which are a huge issue and offer the biggest bang for the buck.”

A proper demand curve should show a clear start and end that corresponds to when the building is occupied. It shouldn’t have any unusual spikes. Common issues identified by the package include:

  • The building starting early or late
  • The building running late
  • The building experiencing peak load outside of occupancy
  • Choppy energy profile (equipment cycling)
  • Peak load within 5% of annual peak

“It’s really a matter of fine-tuning your building schedules and sequences,” Quinn explains. “That’s where the rubber meets the road.”

Even a building being off schedule by 30 minutes or a momentary spike in energy (when all systems come on at once, setting a demand threshold) can have significant impact on usage and cost, Quinn explains.

3) Lower the Curve’s Height
Quinn’s inch-deep, mile-wide strategy allows Duke Realty to take on flexible, incremental retrofits. Once the mile is laid out, Quinn can decide when it’s appropriate to drill down another inch.

“The next opportunity is lowering the height of the curve, which depends on individual loads on the meter,” explains Quinn. “That requires looking at different pieces of equipment, and then you’re really starting to go deeper into the building.”

Quinn has begun collecting data points on each terminal piece of equipment: VAV air handlers and heat pumps with DDC controls. Only a few additional points were collected and combined with about a dozen additional rules. Doing so has already identified the following problems affecting energy usage or tenant comfort:

  • Bad temperature sensors
  • Missing night setbacks
  • Not reaching setpoints
  • Conditioning past the setpoint
  • Short cycling
  • Not reaching airflow
  • Excess after hours demand for conditioning
  • After hours space temperature unchanged

“Those extra measures caused the building to light up like a Christmas tree. There are more issues than you would believe,” Quinn explains. “It would be labor intensive and difficult to find them with traditional techniques. With analytics, they jump right out at you.”

Finding inefficient units and fixing or replacing them brings that energy curve down, and the next goal is to apply analytics to cooling towers, boilers, heat exchangers, and rooftop units, Quinn says.

“It’s all about finding more opportunities,” he adds.

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