A systematic approach to occupancy modeling in ambient sensor-rich buildings

Yang, Zheng; Li, Nan; Becerik-Gerber, Burçin; Orosz, Michael

doi:10.1177/0037549713489918

Cited by 152 publications

(62 citation statements)

References 16 publications

(16 reference statements)

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“…Building heating, ventilation, and air conditioning (HVAC) systems are considered to be a prime tool for energy conservation due to their significant contribution to commercial buildings' energy consumption. For example, Yang evaluates occupancy modeling using twelve ambient sensor variables with results which demonstrate that 20% of gas and 18% of electricity could be saved effectively if occupancy-based demand-response HVAC control is implemented in IB [42]. In energy efficiency analysis, user behavior detection related to the dynamic demands of energy is a critical aspect of supporting the intelligent control scheme of a Building Management System.…”

Section: Second Part-the Optimized Artificial Neural Network Model Wimentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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One of the key requirements for technological systems that are used to secure independent housing for seniors in their home environment is monitoring of daily living activities (ADL), their classification, and recognition of routine daily patterns and habits of seniors in Smart Home Care (SHC). To monitor daily living activities, the use of a temperature, CO 2 , humidity sensors, and microphones are described in experiments in this study. The first part of the paper describes the use of CO 2 concentration measurement for detecting and monitoring room´s occupancy in SHC. In second part focuses this paper on the proposal of an implementation of Artificial Neural Network based on the Levenberg-Marquardt algorithm (LMA) for the detection of human presence in a room of SHC with the use of predictive calculation of CO 2 concentrations from obtained measurements of temperature (indoor, outdoor) T i , T o and relative air humidity rH. Based on the long-term monitoring (1 month) of operational and technical functions (unregulated, uncontrolled) in an experimental Smart Home (SH), LMA was trained through the data picked up by the sensors of CO 2 , T and rH with the aim to indirectly predict CO 2 leading to the elimination of CO 2 sensor from the measurement process. Within the realized experiment, input parameters of the neuronal network and the number of neurons for LMA were optimized on the basis of calculated values of Root Mean Squared Error, the correlative coefficient (R) and the length of the measured training time ANN. With the use of the trained network ANN, we realized a strictly controlled short-term (11 h) experiment without the use of CO2 sensor. Experimental results verified high method accuracy (>95%) within the short-term and long-term experiments for learned ANN (1.6.2015ANN (1.6. -30.6.2015. For learned ANN (1.2.2014ANN (1.2. -27.2.2014) was verified worse method accuracy (>60%). The original contribution is a verification of a low-cost method for the detection of human presence in the real operating environment of SHC. In the third part of the paper is described the practical implementation of voice control of operating technical functions by the KNX technology in SHC by means of the in-house developed application HESTIA, intended for both the desktop system version and the mobile version of the Windows 10 operating system for mobile phones. The resultant application can be configured for any building equipped with the KNX bus system. Voice control implementation is an in-house solution, no third-party software is used here. Utilization of the voice communication application in SHC was proven on the experimental basis with the combination of measurement CO 2 for ADL monitoring in SHC.

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Section: Second Part-the Optimized Artificial Neural Network Model Wimentioning

confidence: 99%

Monitoring of the daily living activities in smart home care

Vaňuš

Belesova

Martínek

et al. 2017

Hum. Cent. Comput. Inf. Sci.

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“…By forming a network, sensor nodes will be able to communicate and exchange information with each other, and the data could be logged in a more organized way simultaneously. Yang et al (2014) explored an improved method to estimate real-time occupancy conditions by utilizing several kinds of environmental sensors including humidity, temperature, carbon dioxide concentration, light, sound, and motion. A numerical model was developed by the researchers on the purpose of estimating occupancy without actually sensing that parameter.…”

Section: Data Acquisition Technologiesmentioning

confidence: 99%

Occupant behavior modeling for smart buildings: A critical review of data acquisition technologies and modeling methodologies

Jia¹,

Srinivasan²

2015

2015 Winter Simulation Conference (WSC)

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At the outset, there is no question that building energy use is largely influenced by the presence and behavior of occupants. Among other, the key to realize energy use reduction while still maintaining occupant comfort is to seamlessly integrate occupant behavior in energy simulation tools with capabilities that would optimally manage building energy systems. This paper provides an in-depth survey of occupant behavior modeling state-of-the-art technologies employed to gather relevant data and modeling methodologies to reduce energy use. Several novel technologies that have been utilized for data collection are discussed in this paper. For the purposes of this review paper, occupant behavior modeling has been organized based on their underlying methodologies namely, statistical analysis; agent -based models; data mining approaches; and stochastic techniques. After providing a thorough review of state-of-the-art research work in the field of occupant behavior modeling for smart, energy efficient buildings, this paper discusses potential areas of improvement.978-1-4673-9743-8/15/$31.00 ©2015 IEEE

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“…In order to achieve maximum energy savings, ECMs need to be designed on a case-by-case basis and in the context of specific physical and functional characteristics of buildings, where building energy models can play a pivotal role. Repeated simulations can be performed to investigate potential opportunities for energy savings [3], identify optimal strategies for daily building operations [4], and select among competing energy retrofit plans [5].…”

Section: Research Motivationmentioning

confidence: 99%

“…Model #2 was built based on actual occupancy schedules observed in the building. For collecting the actual occupancy data, an occupancy detection system proposed by the authors, was used [4]. High-resolution occupancy data of the test bed building was collected and used in the modeling process.…”

Section: Research Motivationmentioning

confidence: 99%

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Impact of Building Occupancy on Assessing the Effectiveness of Energy Conservation Measures

Yang

Tang

et al. 2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Assessing the effectiveness of energy conservation measures (ECMs) prior to their actual implementation in buildings is critical. There is an increasing tendency to employ calibrated building energy models to quantify energy savings that could be achieved by ECMs to justify their implementation. However, there is empirical evidence that reveals noticeable discrepancies between simulated and measured performances of ECMs in buildings. One possible reason for such discrepancies is that actual building occupancy, which is a critical factor that determines the total and peak loads of building systems and related energy consumption, is not represented accurately in most energy models. This paper specifically examines the impact of actual building occupancy on the assessment of ECMs. Two energy models of an office building were built and calibrated with the same audit data following the same procedure, except that one used assumed occupancy and the other used actual occupancy. Both models were used to assess an ECM, which adjusts indoor air temperature set points based on the time of the day. Statistically significant differences were observed in the energy consumptions reported by the two models through paired t-tests. The results highlighted the importance of integrating actual occupancy in the assessment of ECMs. KeywordsEnergy conservation measure; Building energy simulation; Energy model; Occupancy; Calibration BackgroundAssessing the effectiveness of energy conservation measures (ECMs) prior to their actual implementation in buildings is critical for optimizing the ECM designs and providing a baseline that can be used later to gauge their actual performance. With the increased use of building energy simulation for evaluating the effectiveness of various ECMs, one of the most important factors for simulation is to substantiate how well the models represent the characteristics of real buildings [1]. However, there is empirical evidence that reveals discrepancies between simulated and measured performance of the ECMs. One possible reason for such discrepancies is that actual building occupancy, which is a critical factor that determines the total and dynamic loads of building systems and related energy consumption, is not represented in most energy models. This paper examines the impact of building occupancy on the assessment of ECMs. Two energy models of an office building were built and calibrated with the same audit data following the same procedure, except that one used assumed occupancy and the other used actual occupancy. Both models were used to assess the same ECM, which switches the heating, ventilation and air conditioning (HVAC) system in the building between on and off modes based on the time of the day. Described below are our research motivation, research methodology and our findings, followed by our conclusions. Research MotivationThere is an increasing tendency to employ calibrated building energy models, believed to accurately reflect the physical characteristics and internal dynamics of bu...

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A systematic approach to occupancy modeling in ambient sensor-rich buildings

Cited by 152 publications

References 16 publications

Monitoring of the daily living activities in smart home care

Monitoring of the daily living activities in smart home care

Occupant behavior modeling for smart buildings: A critical review of data acquisition technologies and modeling methodologies

Impact of Building Occupancy on Assessing the Effectiveness of Energy Conservation Measures

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