Operating optimization of air-conditioning water system in a subway station using data mining and dynamic system models
Energy-conservation potential in the air-condition water system for subway stations is huge due to its conservative design method. Also, for operation strategy of such systems, the operation modes are formulated with the fixed schedule. This paper presents a data-based optimization method to obtain...
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| Published in | Journal of Building Engineering Vol. 44; p. 103379 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.12.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-7102 2352-7102 |
| DOI | 10.1016/j.jobe.2021.103379 |
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| Abstract | Energy-conservation potential in the air-condition water system for subway stations is huge due to its conservative design method. Also, for operation strategy of such systems, the operation modes are formulated with the fixed schedule. This paper presents a data-based optimization method to obtain optimal parameters of the system for feedforward control. The data mining models are established by using the data from energy consumption platform of the refrigerating system. The study utilized the box-plot method, kNN algorithm and k-means algorithm to process and repair original data. Then Artificial Neural Network (ANN) model is adopted to developed the forecasting model to assess load, performance and energy consumption of the system. The input features of the models are determined by the existed models and clustering analysis. The optimal parameters under the conditions of different load-ratio range and ambient thermal environments are calculated via Genetic Algorithm and trained equipment models. And the optimal parameters are applied to establish operation schedule based on feedforward control and response time. The optimal feedforward control method is verified by a validated TRNSYS model. When the parameters are optimized, the water system energy consumption can be save by 9.5% in a cooling season.
•A data mining model-based optimization process is proposed.•Responses performance of the regulating actions is obtained by dynamic data.•Feedforward control method using optimal operating schedule is adopted.•System energy conservation rate in the cooling season reaches to 9.48%. |
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| AbstractList | Energy-conservation potential in the air-condition water system for subway stations is huge due to its conservative design method. Also, for operation strategy of such systems, the operation modes are formulated with the fixed schedule. This paper presents a data-based optimization method to obtain optimal parameters of the system for feedforward control. The data mining models are established by using the data from energy consumption platform of the refrigerating system. The study utilized the box-plot method, kNN algorithm and k-means algorithm to process and repair original data. Then Artificial Neural Network (ANN) model is adopted to developed the forecasting model to assess load, performance and energy consumption of the system. The input features of the models are determined by the existed models and clustering analysis. The optimal parameters under the conditions of different load-ratio range and ambient thermal environments are calculated via Genetic Algorithm and trained equipment models. And the optimal parameters are applied to establish operation schedule based on feedforward control and response time. The optimal feedforward control method is verified by a validated TRNSYS model. When the parameters are optimized, the water system energy consumption can be save by 9.5% in a cooling season.
•A data mining model-based optimization process is proposed.•Responses performance of the regulating actions is obtained by dynamic data.•Feedforward control method using optimal operating schedule is adopted.•System energy conservation rate in the cooling season reaches to 9.48%. |
| ArticleNumber | 103379 |
| Author | Huang, Yixiang Luo, Yanping Wang, Lei Su, Xing Tian, Shaochen |
| Author_xml | – sequence: 1 givenname: Xing orcidid: 0000-0001-8532-1142 surname: Su fullname: Su, Xing email: suxing@tongji.edu.cn organization: School of Mechanical Engineering, Tongji University, Shanghai, 200092, China – sequence: 2 givenname: Yixiang orcidid: 0000-0002-8695-5857 surname: Huang fullname: Huang, Yixiang organization: School of Mechanical Engineering, Tongji University, Shanghai, 200092, China – sequence: 3 givenname: Lei surname: Wang fullname: Wang, Lei organization: School of Mechanical Engineering, Tongji University, Shanghai, 200092, China – sequence: 4 givenname: Shaochen surname: Tian fullname: Tian, Shaochen organization: School of Mechanical Engineering, Tongji University, Shanghai, 200092, China – sequence: 5 givenname: Yanping surname: Luo fullname: Luo, Yanping organization: Guangzhou Metro Design and Research Institute Company Limited, Guangzhou, 440104, China |
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