书名:Development of a Simulation Tool for the Intelligent Building Agents Project
出版时间:2024
出版社:National Institute of Standards and Technology
前言
In the U.S., commercial buildings are responsible for approximately 36 % of total energy consumption, and the heating, ventilation, and air-conditioning (HVAC) systems make up about 52 % of that total. Improving building operations can significantly reduce the amount and cost of the energy used in the commercial building sector. The Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST) was designed to emulate the air and hydronic systems in a small commercial building primarily to develop and study advanced control approaches for HVAC systems. This report focuses on the calibration and validation of a simulation tool that couples a model of the IBAL with a virtual building model. That tool, IBASIM, will be used to quickly evaluate different control approaches before selecting and implementing the most promising approaches in the IBAL.
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目录
1. Introduction 1
2. Model Overview 3
2.1. IBAL Data 5
2.2. HSIM 12
2.3.ISIM 12
2.4. Model Limitations 16
3. Component Calibration and Validation 18
3.1. Metrics 18
3.2. Component level calibration 19
3.2.1. Chiller models 19
3.2.2. Hydronic flow, pressure, and pump power 27
3.2.2.1. Overview of the flow calculations 27
3.2.2.2. Parameters for the flow calculations 30
3.2.2.3. Pump power consumption 31
3.2.2.4. Validation 32
3.2.3. AHU fan power consumption 34
3.2.4. AHU cooling coil 35
4. Added Controllers 38
4.1. Valve controllers 38
4.2. Secondary loop operation 39
4.3. Chiller staging 40
4.4. TES operation 43
4.5. Chilled water temperature setpoint 44
4.6. Airflow setpoint 45
4.7. AHU static pressure setpoint 45
4.8. Candidates for advanced control approaches 46
5. System-level Validation 47
5.1. Overall performance 48
5.2. Component performance 53
5.2.1. Chiller models 54
5.2.2. Hydronic flow 55
5.2.3. Pump power 57
5.2.4. AHU fan power 57
5.2.5. AHU cooling coil 58
5.2.6. Control decisions 59
6. Conclusion 61
References 62
Appendix A. IBAL Data Dictionary 64
Appendix B. List of Symbols, Abbreviations, and Acronyms 67
List of Tables
Table 1. Setpoints from HSIM 12
Table 2. IBAL controllers and measurements implemented in the MATLAB portion of ISIM 16
Table 3. Variable definitions for chiller equations 20
Table 4. Lower and upper bounds on chiller parameters for optimization. 21
Table 5. Calibrated parameter values for chiller models 23
Table 6. Datasets for chiller calibration. 23
Table 7. Parameter values for the valves in Type 9031. 31
Table 8. Parameter values for the pump differential pressure in Type 9031. 31
Table 9. Parameter values for the pump power in Type 9031 32
Table 10. Datasets for validating the AHU fan power models. 34
Table 11. Parameters in the cooling coil models for the AHUs 35
Table 12. Settings for the PI logic for valves. 38
Table 13. Settings for the PI logic for the VAVs in cooling mode. 45
Table 14. Test cases for system-level validation. All test locations are Atlanta, GA, US 47
Table 15. Number of outliers as a percentage of the total data points and the median percent error. 49
Table 16. Models for further development 61
Table 17. Definitions of measurements 64
List of Figures
Figure 1 Components of the entire IBASIM simulation space 5
Figure 2 Schematic of the hydronic system, with a focus on the primary loop. 7
Figure 3 Schematic of the hydronic system, with a focus on the secondary loop. 8
Figure 4 Overview of the air system. 9
Figure 5 Focus on the AHUs in the air system. 10
Figure 6 Focus on the VAVs in the air system. 11
Figure 7 Overview of the TRNSYS model implemented in Simulation Studio. 14
Figure 8 Details of the (a) AHU1 and (b) AHU2 macros. 15
Figure 9 Chiller model results 24
Figure 10 Comparison between data and simulation model for chiller power and chilled water temperature. 25
Figure 11 Comparison between data and simulation model for chiller power and chilled water temperature with large transients removed 25
Figure 12 Close-up of a period of cycling in Chiller1 26
Figure 13 Pseudocode describing the process to calculate the flow rates in the SL. 28
Figure 14 Pseudocode describing the process to calculate the flow rates in the SL. 28
Figure 15 Pseudocode for calculating flows in the PL. 29
Figure 16 Pump power results 33
Figure 17 Comparison of the simulation results for the pump powers to the measured values 33
Figure 18 Results for AHU fan power consumption model. 35
Figure 19 Results for AHU cooling coil models 37
Figure 20 Pseudocode for setting the bias term for the cooling coil valves. 39
Figure 21 Pseudocode for determining if there is a call for cooling that requires the secondary loop to operate. 40
Figure 22 Pseudocode for chiller staging. 42
Figure 23 Pseudocode for determining if the chiller stage should be changed. 43
Figure 24 Pseudocode for determining the chilled water temperature setpoint, which is used by the chiller and the SL. 44
Figure 25 Boxplots of the percent error in the power between the simulation and the data. The y-axes cut off some of the outliers. 49
Figure 26 Normalized total power consumption 50
Figure 27 Normalized power for individual components. 51
Figure 28 Normalized cost 52
Figure 29 Airflow and temperature data related to the underprediction of fan power during the precooling period for the ex_shift case 53
Figure 30 Chiller1 performance for seven cases under system-level validation 54
Figure 31 Chiller2 performance for seven cases under system-level validation 55
Figure 32 Hydronic flow results when used in the system-level validation 56
Figure 33 Valve model results when used in system-level validation. 56
Figure 34 Pump power results when used in system-level validation. 57
Figure 35 AHU fan power results when used in system-level validation 57
Figure 36 Performance metrics for the inputs to the AHU1 fan power model. 58
Figure 37 Performance metrics for the inputs to the AHU2 fan power model. 58
Figure 38 AHU cooling coil results when used in system-level validation. 59
Figure 39 Performance of the on/off control decisions 60
Figure 40 VAV airflow rate results when used in system-level validation 60
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