Likins, Madison Marie (2018-12). A Comparison of WinAM and EnergyPlus Predicted Consumption Due to the Effects of Thermal Mass and Temperature Setback. Master's Thesis. Thesis uri icon

abstract

  • The purpose of this research was to compare the energy consumption of WinAM and EnergyPlus when thermal mass and a temperature setback are applied. Since WinAM does not account for thermal mass, a correction method was developed to correct the predicted savings produced by a temperature setback. This correction method accounts for thermal mass, wall resistance, building size, and wall area, and works best for climates with a wide range of temperatures. Hourly cooling coil and heating coil energy were plotted versus outside temperature for WinAM and EnergyPlus with varying wall constructions, climates, and temperature schedules, totaling 18 EnergyPlus simulations and 6 WinAM simulations. Consumption from these results were summed to calculate the monthly cooling and coil energy. For each simulation, the difference between energy consumption for a temperature setback and no setback were calculated for each month; this value is the predicted savings produced each monthly by implementing a temperature setback. The difference in predicted savings between WinAM and EnergyPlus was then plotted versus outdoor air temperature. This was used to create the correction method that adjusts WinAM predicted savings to better match EnergyPlus predicted savings. Results indicate WinAM under predicting hourly cooling and heating coil energy. Results also show WinAM over-estimating the predicted savings due to temperature setback by 200 1000 Btu/ft? depending on the temperature. By implementing the WinAM correction method, the WinAM over-estimation is reduced to 30-150 Btu/ft?. The calculated percent reduction in the difference between EnergyPlus and WinAM predicted savings is up to 99%. The large reduction in the difference between WinAM and EnergyPlus predicted savings indicates the correction method works well for the simulations produced. Implementing the correction method leads to a WinAM model that more accurately predicts temperatures setback savings when thermal mass is applied.
  • The purpose of this research was to compare the energy consumption of WinAM and
    EnergyPlus when thermal mass and a temperature setback are applied. Since WinAM does not
    account for thermal mass, a correction method was developed to correct the predicted savings
    produced by a temperature setback. This correction method accounts for thermal mass, wall
    resistance, building size, and wall area, and works best for climates with a wide range of
    temperatures.

    Hourly cooling coil and heating coil energy were plotted versus outside temperature for
    WinAM and EnergyPlus with varying wall constructions, climates, and temperature schedules,
    totaling 18 EnergyPlus simulations and 6 WinAM simulations. Consumption from these results
    were summed to calculate the monthly cooling and coil energy. For each simulation, the
    difference between energy consumption for a temperature setback and no setback were
    calculated for each month; this value is the predicted savings produced each monthly by
    implementing a temperature setback. The difference in predicted savings between WinAM and
    EnergyPlus was then plotted versus outdoor air temperature. This was used to create the
    correction method that adjusts WinAM predicted savings to better match EnergyPlus predicted
    savings.

    Results indicate WinAM under predicting hourly cooling and heating coil energy. Results
    also show WinAM over-estimating the predicted savings due to temperature setback by 200
    1000 Btu/ft? depending on the temperature. By implementing the WinAM correction method, the
    WinAM over-estimation is reduced to 30-150 Btu/ft?. The calculated percent reduction in the
    difference between EnergyPlus and WinAM predicted savings is up to 99%.

    The large reduction in the difference between WinAM and EnergyPlus predicted savings
    indicates the correction method works well for the simulations produced. Implementing the
    correction method leads to a WinAM model that more accurately predicts temperatures setback
    savings when thermal mass is applied.

publication date

  • December 2018