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Ap-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method

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Part of the book series:Advances in Intelligent Systems and Computing ((AISC,volume 890 ))

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Abstract

Considering that existing device-based occupant detection methods cannot count those who do not carry a device, in this paper, for buildings where the behaviour of the occupants tends to be regular, taking the WiFi-based occupant detection method as a basis, we propose ap-persistent frequent itemsets with 1-right-hand-side (RHS)-based occupant detection algorithm to improve the occupant detection performance in terms of accuracy. Association analysis using apriori algorithm is utilized to predict the occupancy of buildings through mining the relationships among occupants. We mathematically prove the reasonability of frequent itemsets with 1-RHS chosen in our algorithm and show the experimental results of applying this approach with differentp. The results show that our proposed method can improve the accuracy performance in that it can see the occupant in buildings that the WiFi-based occupant detection method cannot see.

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Acknowledgements

This work was supported by National Key Research and Development Project of China, No. 2017YFC0704100 (entitled New generation intelligent building platform techniques), National Experimental Teaching Demonstration Center (entitled Building Control and Energy Saving Optimization Experiment Center, Anhui Jianzhu University), National Natural Science Foundation of China (Grant No. 11471304), and Ph.D. Research Startup Foundation of Anhui Jianzhu University (Grant No. 2017QD07).

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Authors and Affiliations

  1. Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving, Anhui Jianzhu University, Hefei, Anhui, China

    Ping Wang, Huaqian Cao, Si Chen, Jiake Li, Chang Tu & Zhenya Zhang

  2. School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui, China

    Ping Wang, Huaqian Cao, Si Chen, Jiake Li, Chang Tu & Zhenya Zhang

Authors
  1. Ping Wang

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  2. Huaqian Cao

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  3. Si Chen

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  4. Jiake Li

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  5. Chang Tu

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  6. Zhenya Zhang

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Corresponding author

Correspondence toZhenya Zhang.

Editor information

Editors and Affiliations

  1. Anhui Jianzhu University, Hefei, China

    Qiansheng Fang

  2. University of the West of England, Bristol, UK

    Quanmin Zhu

  3. Shenyang Jianzhu University, Shenyang, China

    Feng Qiao

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Wang, P., Cao, H., Chen, S., Li, J., Tu, C., Zhang, Z. (2019). Ap-Persistent Frequent Itemsets with 1-RHS Based Correction Algorithm for Improving the Performance of WiFi-Based Occupant Detection Method. In: Fang, Q., Zhu, Q., Qiao, F. (eds) Advancements in Smart City and Intelligent Building. ICSCIB 2018. Advances in Intelligent Systems and Computing, vol 890 . Springer, Singapore. https://doi.org/10.1007/978-981-13-6733-5_46

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eBook
JPY 17159
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
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Softcover Book
JPY 21449
Price includes VAT (Japan)
  • Compact, lightweight edition
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Tax calculation will be finalised at checkout

Purchases are for personal use only

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