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A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Author

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  • Dana-Mihaela Petroșanu

    (Department of Mathematics-Informatics, Faculty of Applied Sciences, University Politehnica of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania)

  • George Căruțașu

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, 012101 Bucharest, Romania
    Doctoral School, University Politehnica of Timișoara, Piața Victoriei 2, 300006 Timișoara, Romania)

  • Nicoleta Luminița Căruțașu

    (Department of Robotics and Production Systems, Faculty of Industrial Engineering and Robotics, University Politehnica of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania)

  • Alexandru Pîrjan

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, 012101 Bucharest, Romania)

Abstract

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

Suggested Citation

  • Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4745-:d:297314
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    References listed on IDEAS

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    1. Federico Divina & Miguel García Torres & Francisco A. Goméz Vela & José Luis Vázquez Noguera, 2019. "A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings," Energies, MDPI, vol. 12(10), pages 1-23, May.
    2. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    3. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    4. Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
    5. Marco Fagiani & Stefano Squartini & Leonardo Gabrielli & Marco Severini & Francesco Piazza, 2016. "A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids," Energies, MDPI, vol. 9(9), pages 1-25, August.
    6. Seokho Kim & Yujin Song & Yoondong Sung & Donghyun Seo, 2019. "Development of a Consecutive Occupancy Estimation Framework for Improving the Energy Demand Prediction Performance of Building Energy Modeling Tools," Energies, MDPI, vol. 12(3), pages 1-21, January.
    7. Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.
    8. Roberto Casado-Vara & Zita Vale & Javier Prieto & Juan M. Corchado, 2018. "Fault-Tolerant Temperature Control Algorithm for IoT Networks in Smart Buildings," Energies, MDPI, vol. 11(12), pages 1-17, December.
    9. Jinghuan Guo & Yong Mu & Mudi Xiong & Yaqing Liu & Jingxuan Gu, 2019. "Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes," Complexity, Hindawi, vol. 2019, pages 1-10, March.
    10. Nivine Attoue & Isam Shahrour & Rafic Younes, 2018. "Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting," Energies, MDPI, vol. 11(2), pages 1-12, February.
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    Cited by:

    1. Pedro Macieira & Luis Gomes & Zita Vale, 2021. "Energy Management Model for HVAC Control Supported by Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.

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