Ma, Liang (2011-12). Methodology to Analyze the Sensitivity of Building Energy Consumption to HVAC System Sensor Error. Master's Thesis. Thesis uri icon

abstract

  • This thesis proposes a methodology for determining sensitivity of building energy consumption of HVAC systems to sensor error. It is based on a series of simulations of a generic building, the model for which is based on several typical input parameters. There are a total of eight scenarios considered in this simulation. The simulation tool was developed based on Excel. The control parameters examined include room temperature, cold deck temperature, hot deck temperature, pump pressure, and fan pressure. All of the parameters considered are varied in order to analyze the sensitivity of building energy consumption to their variation. In this tool, different operation schedules for equipment, occupancy, and lighting are considered. By changing each control parameter, the sensitivity of energy use to sensor error is simulated, a regression model is generated, and the energy consumption change is expressed as a function of sensor error and outside air percentage. Two applications of this methodology are presented in this thesis. One is a SDVAV system and the other is a DDVAV system. The outside air percentage changes the trend of the sensor error curve. After the sensitivity study is discussed, some recommendations regarding the calibration intervals of the sensors are given.
  • This thesis proposes a methodology for determining sensitivity of building energy consumption of HVAC systems to sensor error. It is based on a series of simulations of a generic building, the model for which is based on several typical input parameters.

    There are a total of eight scenarios considered in this simulation. The simulation tool was developed based on Excel. The control parameters examined include room temperature, cold deck temperature, hot deck temperature, pump pressure, and fan pressure. All of the parameters considered are varied in order to analyze the sensitivity of building energy consumption to their variation. In this tool, different operation schedules for equipment, occupancy, and lighting are considered. By changing each control parameter, the sensitivity of energy use to sensor error is simulated, a regression model is generated, and the energy consumption change is expressed as a function of sensor error and outside air percentage.

    Two applications of this methodology are presented in this thesis. One is a SDVAV system and the other is a DDVAV system. The outside air percentage changes the trend of the sensor error curve.

    After the sensitivity study is discussed, some recommendations regarding the calibration intervals of the sensors are given.

publication date

  • December 2011