Estimation of occupant distribution by detecting the entrance and leaving events of zones in building

Wang, Hengtao; Jia, Qing‐Shan; Lei, Yulin; Zhao, Qianchuan; Guan, Xiaohong

doi:10.1109/mfi.2012.6343074

Cited by 9 publications

(6 citation statements)

References 17 publications

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“…The initial policy d 1 is selected as a naive estimation, which increases the estimation when E C k = e 1 , reduces it when E C k = e 2 , and keeps it unchanged otherwise. This policy is popularly adopted in commercial sensing systems, but is known to have accumulative errors [33,34]. We consider five groups of experiments.…”

Section: Numerical Resultsmentioning

confidence: 99%

Event-based optimization with random packet dropping

Jia

Tang

Lang

2020

Sci. China Inf. Sci.

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Event-based optimization (EBO) provides a general framework for policy optimization in many discrete event dynamic systems where decision making is triggered by events that represent state transitions with common features. Because the number of events can be defined by the user and usually increases linearly with respect to the system scale, EBO has good potential to address large-scale problems where the state space grows exponentially. However, in many practical systems, sensors are geographically distributed and connected to a central controller through imperfect communication channels. Therefore, events observed by the sensors may not reach the controller. Optimization methods of event-based policies in these cases have not yet been identified. In this paper, we consider this important problem and make three major contributions. First, we formulate a mathematical EBO model in which the communication between sensors and controllers is subject to random packet dropping. Second, we show that this EBO model can be converted to another EBO model with perfect communication. Then, the performance difference equation and the performance derivative equation for event-based policies are straightforward to develop. One gradient-based policy iteration algorithm is developed for problems where the state transition probabilities are explicitly known, while another for problems where they are unknown. Third, the performance of the algorithms and the impact of the packet dropping probability on policy performance are numerically demonstrated on a single-zone occupant level estimation problem in buildings.

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Section: Numerical Resultsmentioning

confidence: 99%

Event-based optimization with random packet dropping

Jia

Tang

Lang

2020

Sci. China Inf. Sci.

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“…In this section, we derive an estimator of as a function of the measurements and and the estimated room dynamics , , . Let (10) and consider the levels prediction error (11) Under the stated assumptions is a zero-mean Gaussian white noise [41]. Substituting (10) into (11) and rearranging properly, we obtain (12) where the unknowns are only and , since can be computed given the available information.…”

Section: Deconvolution Of the Occupancy Levelsmentioning

confidence: 99%

Regularized Deconvolution-Based Approaches for Estimating Room Occupancies

Ebadat¹,

Bottegal²,

Varagnolo

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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We address the problem of estimating the number of people in a room using information available in standard HVAC systems. We propose an estimation scheme based on two phases. In the first phase, we assume the availability of pilot data and identify a model for the dynamic relations occurring between occupancy levels, concentration and room temperature. In the second phase, we make use of the identified model to formulate the occupancy estimation task as a deconvolution problem. In particular, we aim at obtaining an estimated occupancy pattern by trading off between adherence to the current measurements and regularity of the pattern. To achieve this goal, we employ a special instance of the so-called fused lasso estimator, which promotes piecewise constant estimates by including an norm-dependent term in the associated cost function. We extend the proposed estimator to include different sources of information, such as actuation of the ventilation system and door opening/closing events. We also provide conditions under which the occupancy estimator provides correct estimates within a guaranteed probability. We test the estimator running experiments on a real testbed, in order to compare it with other occupancy estimation techniques and assess the value of having additional information sources. Note to Practitioners-Home automation systems benefit from automatic recognition of human presence in the built environment.Since dedicated hardware is costly, it may be preferable to detect occupancy with software-based systems which do not require the installation of additional devices. The object of this study is the reconstruction of occupancy patterns in a room using measurements of concentration, temperature, fresh air inflow, and door opening/closing events. All these signals are information sources often available in HVAC systems of modern buildings and homes. We assess the value of such information sources in terms of their relevance in detecting occupancy in small and medium-sized rooms. The proposed estimation scheme is composed of two distinct phases. The first is a training phase where the goal is to de-Manuscript

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“…When there are many occupants, the uncertainty of occupants' movement may have a great impact on building load. Based on our previous work in [14] and [15], we can use the statistical data from the RFID system to estimate the occupants' distribution in different buildings. Then scenario tree is also used to illustrate the uncertainty of occupants distribution in the following section.…”

Section: Uncertainties: Solar Power and Building Loadmentioning

confidence: 99%

“…There are 1000 occupants and their distribution is estimated as shown in Table. 1 (mainly based on students' activity). In our previous work [14] and [15], RFID system is used to estimate the occupants' distribution. The historical statistical data can be used to establish a more accurate distribution model.…”

Section: Description Of the Test Casementioning

confidence: 99%

Optimal storage battery scheduling for energy-efficient buildings in a microgrid

Zhang

Jia

2015

The 27th Chinese Control and Decision Conference (2015 CCDC)

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Residential and commercial buildings consume more than 40% of electrical power and contribute a large percentage of CO2 and SO2. In order to alleviate energy crises and environment deterioration, improving energy efficiency of buildings has become significant and important. This paper considers the storage battery scheduling problem in a networked building microgrid with decentralized solar power and storage battery. First, we formulate an optimization model with minimizing overall electricity cost as the objective. Then we use a linearization technique to simplify the model and reformulate the optimization problem as a deterministic programming and further a stochastic programming with scenario tree being used to handle the uncertainties of solar power and building load. At last, test results of a toy model verify the effectiveness of the proposed method and provide insight on power scheduling in a networked microgrid for improving building energy efficiency.

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Estimation of occupant distribution by detecting the entrance and leaving events of zones in building

Cited by 9 publications

References 17 publications

Event-based optimization with random packet dropping

Event-based optimization with random packet dropping

Regularized Deconvolution-Based Approaches for Estimating Room Occupancies

Optimal storage battery scheduling for energy-efficient buildings in a microgrid

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