Sensor impacts on building and HVAC controls: A critical review for building energy performance

Bae, Yeonjin; Bhattacharya, Saptarshi; Cui, Borui; Lee, Seungjae; Li, Yanfei; Zhang, Liang; Im, Piljae; Adetola, Veronica; Vrabie, Draguna; Leach, Matt; Kuruganti, Teja

doi:10.1016/j.adapen.2021.100068

Cited by 78 publications

(18 citation statements)

References 88 publications

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“…They collect desired information to input into controls for the completion of subsequent control actions. When sensors work in unhealthy or faulty conditions, the benefits of the control benefits will be compromised regardless of the control's quality [1]. For buildings, multiple components directly influence the sensor placement and deployment, such as sensor errors, sensor locations, sensor types, and sensor costs.…”

Section: Introductionmentioning

confidence: 99%

Impacts of New Sensor Types for Selected Advanced Controls

Li¹,

Yoon²,

Amasyali³

et al. 2022

Self Cite

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Section: Introductionmentioning

confidence: 99%

Impacts of New Sensor Types for Selected Advanced Controls

Li¹,

Yoon²,

Amasyali³

et al. 2022

Self Cite

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“…In the past 10 years, the research focus has been on advanced controls, and sensors were not well studied. When sensors work in unhealthy or faulty conditions, the control benefits will be compromised regardless of the promising benefits of the controls [1]. For buildings, multiple components directly influence the sensor placement and deployment, such as sensor errors, sensor locations, sensor types, and sensor costs.…”

Section: Introductionmentioning

confidence: 99%

Sensor Impact Evaluation at Different Sensor Locations in a Multi-zone Office Building

Yoon

et al. 2022

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“…However, indoor positioning has a very important application in the field of architecture. Building emergency management [ 3 ], smart plug load control [ 4 ], and smart HVAC controls [ 5 ] are just a few typical examples. Indoor positioning has become a research hotspot.…”

Section: Introductionmentioning

confidence: 99%

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

Cheng

Niu

Zhang

et al. 2022

Sensors

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With the intense deployment of wireless systems and the widespread use of intelligent equipment, the requirement for indoor positioning services is increasing, and Wi-Fi fingerprinting has emerged as the most often used approach to identifying indoor target users. The construction time of the Wi-Fi received signal strength (RSS) fingerprint database is short, but the positioning performance is unstable and susceptible to noise. Meanwhile, to strengthen indoor positioning precision, a fingerprints algorithm based on a convolution neural network (CNN) is often used. However, the number of reference points participating in the location estimation has a great influence on the positioning accuracy. There is no standard for the number of reference points involved in position estimation by traditional methods. For the above problems, the grayscale images corresponding to RSS and angle of arrival are fused into RGB images to improve stability. This paper presents a position estimation method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which can select appropriate reference points according to the situation. DBSCAN analyses the CNN output and can choose the number of reference points based on the situation. Finally, the position is approximated using the weighted k-nearest neighbors. The results show that the calculation error of our proposed method is at least 0.1–0.3 m less than that of the traditional method.

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Sensor impacts on building and HVAC controls: A critical review for building energy performance

Cited by 78 publications

References 88 publications

Impacts of New Sensor Types for Selected Advanced Controls

Impacts of New Sensor Types for Selected Advanced Controls

Sensor Impact Evaluation at Different Sensor Locations in a Multi-zone Office Building

Improved CNN-Based Indoor Localization by Using RGB Images and DBSCAN Algorithm

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