Yang, Ying (2013-08). A Study of Predicted Energy Savings and Sensitivity Analysis. Master's Thesis. Thesis uri icon

abstract

  • The sensitivity of the important inputs and the savings prediction function reliability for the WinAM 4.3 software is studied in this research. WinAM was developed by the Continuous Commissioning (CC) group in the Energy Systems Laboratory at Texas A&M University. For the sensitivity analysis task, fourteen inputs are studied by adjusting one input at a time within ? 30% compared with its baseline. The Single Duct Variable Air Volume (SDVAV) system with and without the economizer has been applied to the square zone model. Mean Bias Error (MBE) and Influence Coefficient (IC) have been selected as the statistical methods to analyze the outputs that are obtained from WinAM 4.3. For the saving prediction reliability analysis task, eleven Continuous Commissioning projects have been selected. After reviewing each project, seven of the eleven have been chosen. The measured energy consumption data for the seven projects is compared with the simulated energy consumption data that has been obtained from WinAM 4.3. Normalization Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Squared Error (CV (RMSE)) statistical methods have been used to analyze the results from real measured data and simulated data. Highly sensitive parameters for each energy resource of the system with the economizer and the system without the economizer have been generated in the sensitivity analysis task. The main result of the savings prediction reliability analysis is that calibration improves the model's quality. It also improves the predicted energy savings results compared with the results generated from the uncalibrated model.
  • The sensitivity of the important inputs and the savings prediction function reliability for the WinAM 4.3 software is studied in this research. WinAM was developed by the Continuous Commissioning (CC) group in the Energy Systems Laboratory at Texas A&M University. For the sensitivity analysis task, fourteen inputs are studied by adjusting one input at a time within ? 30% compared with its baseline. The Single Duct Variable Air Volume (SDVAV) system with and without the economizer has been applied to the square zone model. Mean Bias Error (MBE) and Influence Coefficient (IC) have been selected as the statistical methods to analyze the outputs that are obtained from WinAM 4.3. For the saving prediction reliability analysis task, eleven Continuous Commissioning projects have been selected. After reviewing each project, seven of the eleven have been chosen. The measured energy consumption data for the seven projects is compared with the simulated energy consumption data that has been obtained from WinAM 4.3. Normalization Mean Bias Error (NMBE) and Coefficient of Variation of the Root Mean Squared Error (CV (RMSE)) statistical methods have been used to analyze the results from real measured data and simulated data.

    Highly sensitive parameters for each energy resource of the system with the economizer and the system without the economizer have been generated in the sensitivity analysis task. The main result of the savings prediction reliability analysis is that calibration improves the model's quality. It also improves the predicted energy savings results compared with the results generated from the uncalibrated model.

publication date

  • August 2013