Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited Data
Model-based optimization is an important means by which to analyze the energy-saving potential of chiller plants. To obtain reliable energy-saving results, model calibration is essential, which strongly depends on operating data. However, sufficient data cannot always be satisfied in reality. To imp...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/2/918 |
| _version_ | 1797443104982171648 |
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| author | Cheng Zhen Jide Niu Zhe Tian |
| author_facet | Cheng Zhen Jide Niu Zhe Tian |
| author_sort | Cheng Zhen |
| collection | DOAJ |
| description | Model-based optimization is an important means by which to analyze the energy-saving potential of chiller plants. To obtain reliable energy-saving results, model calibration is essential, which strongly depends on operating data. However, sufficient data cannot always be satisfied in reality. To improve the prediction accuracy of the model with limited data, a model calibration method based on error reverse correction was investigated. A traditional optimization-based calibration method was first used for preliminary model calibration to obtain simulation data and simulation errors. Then, the sources of the simulation errors were analyzed to determine the distribution characteristics of the corresponding operating conditions of the model. Finally, the performance of the model was reversely corrected by adding a correction term to the original model. The proposed calibration method was tested on a chiller plant in Xiamen, China. The results showed that the proposed calibration method improved prediction accuracy by 2.61% (the coefficient of variation of the root mean square error (CV (RMSE)) was reduced from 3.96% to 1.35%) compared to the traditional method. The maximum mean bias error (MBE) for monthly chiller energy consumption was 2.66% with the proposed calibration method, while it was 10.42% with the traditional method. Overall, in scenarios with limited data, the proposed calibration method can effectively improve the accuracy of simulation results. |
| first_indexed | 2024-03-09T12:51:19Z |
| format | Article |
| id | doaj.art-cc9f878c73d34e43805ea2e8d02ff03e |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T12:51:19Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-cc9f878c73d34e43805ea2e8d02ff03e2023-11-30T22:05:57ZengMDPI AGEnergies1996-10732023-01-0116291810.3390/en16020918Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited DataCheng Zhen0Jide Niu1Zhe Tian2School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Environmental Science and Engineering, Tianjin University, Tianjin 300072, ChinaModel-based optimization is an important means by which to analyze the energy-saving potential of chiller plants. To obtain reliable energy-saving results, model calibration is essential, which strongly depends on operating data. However, sufficient data cannot always be satisfied in reality. To improve the prediction accuracy of the model with limited data, a model calibration method based on error reverse correction was investigated. A traditional optimization-based calibration method was first used for preliminary model calibration to obtain simulation data and simulation errors. Then, the sources of the simulation errors were analyzed to determine the distribution characteristics of the corresponding operating conditions of the model. Finally, the performance of the model was reversely corrected by adding a correction term to the original model. The proposed calibration method was tested on a chiller plant in Xiamen, China. The results showed that the proposed calibration method improved prediction accuracy by 2.61% (the coefficient of variation of the root mean square error (CV (RMSE)) was reduced from 3.96% to 1.35%) compared to the traditional method. The maximum mean bias error (MBE) for monthly chiller energy consumption was 2.66% with the proposed calibration method, while it was 10.42% with the traditional method. Overall, in scenarios with limited data, the proposed calibration method can effectively improve the accuracy of simulation results.https://www.mdpi.com/1996-1073/16/2/918model calibrationlimited dataModelicachiller plant |
| spellingShingle | Cheng Zhen Jide Niu Zhe Tian Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited Data Energies model calibration limited data Modelica chiller plant |
| title | Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited Data |
| title_full | Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited Data |
| title_fullStr | Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited Data |
| title_full_unstemmed | Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited Data |
| title_short | Research on Model Calibration Method of Chiller Plants Based on Error Reverse Correction with Limited Data |
| title_sort | research on model calibration method of chiller plants based on error reverse correction with limited data |
| topic | model calibration limited data Modelica chiller plant |
| url | https://www.mdpi.com/1996-1073/16/2/918 |
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