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A Low Energy Consumption Multi-sensor Data Fusion Method for Fan Coil Unit Thermal Performance Test

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Abstract

The multi-sensor network can acquire and analyze the thermal performance data of fan coil unit and other building systems in real time by means of low energy and high precision sensing technology. It is necessary to compress the thermal data in the data transmission process. Aiming at the data fusion process applied to the thermal performance test system of fan coil unit, a new SMART-RR algorithm with low energy consumption data fusion is proposed. Considering the existence of cyclic repeatability and data redundancy, a time interval data fusion strategy of adding repeatability reduction factor is bedded in the algorithm. The simulation results show that the SMART-RR algorithm is a low energy consumption data fusion algorithm with low data communication volume and high accuracy.

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Acknowledgment

This work was supported by the project of National Import Research Priorities Program (2016YFB0801004), Heilongjiang Province Natural Science Youth Fund (QC2012C116), Jiangsu Province Policy Guidance Program (Research Cooperation)-Prospective Joint Research Project (BY2016049-01), Science and Technology Planning Project of Jiangsu Provincial Department of Construction (2015ZD83) and Natural Science Research Project of Universities of Jiangsu Province (16KJB560015).

Special thanks to referees who provided us constant support and help in a previous version of this article.

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Correspondence to Jin Li .

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, C., Li, J., Yu, B., Wang, L. (2018). A Low Energy Consumption Multi-sensor Data Fusion Method for Fan Coil Unit Thermal Performance Test. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_46

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  • DOI: https://doi.org/10.1007/978-3-319-73317-3_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73316-6

  • Online ISBN: 978-3-319-73317-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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