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Development of data-driven performance benchmarking methodology for a large number of bus air conditioners.

Author(s) : CHEN Z., GUO F., XIAO F., JIN X., SHI J., HE W.

Type of article: IJR article

Summary

Bus air conditioners (ACs) are responsible for providing a comfortable cabin environment for passengers. Identifying the bus ACs with degraded performance from a large number of city buses is a critical and challenging task in the development of smart cities. This study developed a data-driven benchmarking methodology to detect anomalous operations with degraded energy performance from a large number of bus ACs. For each target AC to be benchmarked, its similar operation data in other ACs, termed comparable peer samples, are first identified by a Long-Short-Term-Memory (LSTM) autoencoder-based similarity measurement method. The comparable peer samples are then used to develop a LSTM network-based reference model for predicting the power consumption of the target AC. A key energy performance indicator termed power consumption ratio (PCR) is defined for the target AC as the ratio of its measured power to the predicted power. Statistical analysis-based trend and change detection algorithms are designed to identify a trend or change of PCR over a few days for anomalous detection. To validate the benchmarking methodology, two fault experiments were conducted in field-operating bus ACs, and the results show encouraging potentials of the proposed methodology for health monitoring of a large number of ACs serving the city bus fleet.

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Pages: 105-118

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Details

  • Original title: Development of data-driven performance benchmarking methodology for a large number of bus air conditioners.
  • Record ID : 30031537
  • Languages: English
  • Subject: Technology
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 149
  • Publication date: 2023/05
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2022.12.027

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