Two-stage structured learning approach for stable occupancy detection

Liu, Tianchi; Li, Yue; Bai, Zuo; De, Jaydeep; Cao, Le; Lin, Zhiping; Lin, Shing-Jong; Huang, Guang-Bin; Cui, Dongshun

doi:10.1109/ijcnn.2016.7727485

Cited by 11 publications

(3 citation statements)

References 29 publications

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“…Some other models, rarely used, include the Monte Carlo [35], regression and time-series models [36], Dynamic Markov Time-Window Inference (DMTWI), Auto-Regressive Moving Average (ARMA) and Support Vector Regression (SVR) models [42], agent-based models (ABM) [47], Gaussian approach [46,48], and artificial neural networks (ANN) [29,30,49,50]. New approaches like extreme learning machine (ELM) and its modifications [11][12][13][14][15], narrative-based modelling and multi-criteria analysis [20], have also been used recently. Most studies emphasize the need for a more suitable and accurate occupancy prediction model.…”

Section: Previous Related Workmentioning

confidence: 99%

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

et al. 2020

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Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

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Section: Previous Related Workmentioning

confidence: 99%

Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

et al. 2020

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“…Based on the authors’ findings, the KNN-trained classifier was much more effective than the LDA-trained classifier. The authors propose a two-stage ELM method for occupancy estimation using environmental sensors of CO 2 and temperature [ 55 ]. For the first stage, local representations of raw features were obtained using the ELM algorithm.…”

Section: Data Collection Methodsmentioning

confidence: 99%

“…Authors validated their models using the public ECO dataset1 [ 119 ] and found an occupancy detection accuracy ranging from 68 to 94 percent. According to Liu et al [ 55 ], occupancy can be estimated using CO 2 and temperature sensors. First, preliminary detection results were obtained using the ELM algorithm.…”

Section: Data Analysis Approachmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

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In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

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