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Ensemble-based extreme learning machine model for occupancy detection with ambient attributes

Author

Listed:
  • Sachin Kumar

    (University of Delhi)

  • Jagvinder Singh

    (Delhi Technological University)

  • Ompal Singh

    (University of Delhi)

Abstract

Context-aware computing is a growing research domain in present circumstances due to technological advancements in the area of sensors technology, big data, artificial intelligence and robotics and automation. It has many applications for making the daily life of human beings sustainable, comfortable, and smooth. Context ware computing also includes ambient intelligence and applications such as occupancy detection, prediction, user recognition etc. Occupancy detection and recognition help in developing intelligent applications which help the energy management, intelligent decision making, that results in cost reduction and fault and failure prevention of services and products in advance. Several studies have been conducted to detect the occupancy with a different set of methodologies and approaches using varying types of data such as environmental parameters, image and video-based attributes, wireless or sensor based parameters, and noise-based parameters. This paper proposes a reliable, more accurate and efficient model based on the statistical analysis of the sensor based data for occupancy detection. Detailed quantification of the relationship of the ambient attributes is presented and the ensemble model is developed based on machine learning technique extreme learning machine to achieve the significant level of improvement in accuracy, efficiency, generalization and reliability. In addition to this, the paper also proposes one online and adaptive model-based online sequential extreme learning machine to perform occupancy detection on real-time data when complete data is not available and learning is done with recent data points coming in the form of streams. Results are compared with existing work in the domain and it is observed that proposed model perform better in terms of efficiency and accuracy over existing literature work.

Suggested Citation

  • Sachin Kumar & Jagvinder Singh & Ompal Singh, 2020. "Ensemble-based extreme learning machine model for occupancy detection with ambient attributes," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 173-183, July.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-019-00935-1
    DOI: 10.1007/s13198-019-00935-1
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    References listed on IDEAS

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    1. Zheng, Xinye & Wei, Chu & Qin, Ping & Guo, Jin & Yu, Yihua & Song, Feng & Chen, Zhanming, 2014. "Characteristics of residential energy consumption in China: Findings from a household survey," Energy Policy, Elsevier, vol. 75(C), pages 126-135.
    2. Bhattacharya, Mita & Paramati, Sudharshan Reddy & Ozturk, Ilhan & Bhattacharya, Sankar, 2016. "The effect of renewable energy consumption on economic growth: Evidence from top 38 countries," Applied Energy, Elsevier, vol. 162(C), pages 733-741.
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    Citations

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    Cited by:

    1. Andreé Vela & Joanna Alvarado-Uribe & Hector G. Ceballos, 2021. "Indoor Environment Dataset to Estimate Room Occupancy," Data, MDPI, vol. 6(12), pages 1-12, December.
    2. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    3. Sachin Kumar & Shivam Panwar & Jagvinder Singh & Anuj Kumar Sharma & Zairu Nisha, 2022. "iCACD: an intelligent deep learning model to categorise current affairs news article for efficient journalistic process," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2572-2582, October.
    4. Ruchika Malhotra & Megha Khanna, 2023. "On the applicability of search-based algorithms for software change prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 55-73, February.
    5. Sachin Kumar & Aditya Sharma & B Kartheek Reddy & Shreyas Sachan & Vaibhav Jain & Jagvinder Singh, 2022. "An intelligent model based on integrated inverse document frequency and multinomial Naive Bayes for current affairs news categorisation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1341-1355, June.
    6. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).

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