A Robust Occupancy-Based Building Lighting Framework Using Wireless Sensor Networks

Hsiao, Rong Shue; Lin, Ding; Lin, Hsin Piao; Cheng, Shu Chun; Chung, Chen Hua

doi:10.4028/www.scientific.net/amm.284-287.2015

Cited by 4 publications

(4 citation statements)

References 8 publications

(9 reference statements)

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“…The conventional passive infra-red (PIR) sensors can be used to detect a certain wavelength when a person is near the sensor. Previous papers [18,19,20] demonstrated the possibility of applying this to control lighting systems. However, the disadvantage of the PIR sensor becomes evident when the occupant remains stationary for a certain period of time.…”

Section: Occupancy Recognition and Predictionmentioning

confidence: 99%

Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

Aftab

Chen

Chau

et al. 2017

Energy and Buildings

149

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Intelligent building automation systems can reduce the energy consumption of heating, ventilation and air-conditioning (HVAC) units by sensing the comfort requirements automatically and scheduling the HVAC operations dynamically. Traditional building automation systems rely on fairly inaccurate occupancy sensors and basic predictive control using oversimplified building thermal response models, all of which prevent such systems from reaching their full potential. Such limitations can now be avoided due to the recent developments in embedded system technologies, which provide viable low-cost computing platforms with powerful processors and sizeable memory storage in a small footprint. As a result, building automation systems can now efficiently execute highly-sophisticated computational tasks, such as realtime video processing and accurate thermal-response simulations. With this in mind, we designed and implemented an occupancy-predictive HVAC control system in a low-cost yet powerful embedded system (using Raspberry Pi 3) to demonstrate the following key features for building automation: (1) real-time occupancy recognition using videoprocessing and machine-learning techniques, (2) dynamic analysis and prediction of occupancy patterns, and (3) model predictive control for HVAC operations guided by real-time building thermal response simulations (using an on-board EnergyPlus simulator). We deployed and evaluated our system for providing automatic HVAC control in the large public indoor space of a mosque, thereby achieving significant energy savings.

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Section: Occupancy Recognition and Predictionmentioning

confidence: 99%

Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

Aftab

Chen

Chau

et al. 2017

Energy and Buildings

149

View full text Add to dashboard Cite

show abstract

“…In order to solve this problem, additional sensors were implemented in the PIR. Several studies [33,34,35] proposed integrating PIR and other sensors by WSN. Different types of occupancy detection technologies were developed for replacing the PIR sensor.…”

Section: The Development Of Occupancy Detection Technologymentioning

confidence: 99%

Enabling Smart Air Conditioning by Sensor Development: A Review

Cheng

Lee

2016

Sensors

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The study investigates the development of sensors, in particular the use of thermo-fluidic sensors and occupancy detectors, to achieve smart operation of air conditioning systems. Smart operation refers to the operation of air conditioners by the reinforcement of interaction to achieve both thermal comfort and energy efficiency. Sensors related to thermal comfort include those of temperature, humidity, and pressure and wind velocity anemometers. Improvements in their performance in the past years have been studied by a literature survey. Traditional occupancy detection using passive infra-red (PIR) sensors and novel methodologies using smartphones and wearable sensors are both discussed. Referring to the case studies summarized in this study, air conditioning energy savings are evaluated quantitatively. Results show that energy savings of air conditioners before 2000 was 11%, and 30% after 2000 by the integration of thermo-fluidic sensors and occupancy detectors. By utilizing wearable sensing to detect the human motions, metabolic rates and related information, the energy savings can reach up to 46.3% and keep the minimum change of predicted mean vote (∆PMV→0), which means there is no compromise in thermal comfort. This enables smart air conditioning to compensate for the large variations from person to person in terms of physiological and psychological satisfaction, and find an optimal temperature for everyone in a given space. However, this tendency should be evidenced by more experimental results in the future.

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“…The proposed multisensor fusion algorithm. We modified the dual technology sensor, presented in [4], by adding a reed switch. Also, the multisensor fusion inference algorithm is developed, as shown in Fig.…”

Section: Techniques Of Multisensory Fusionmentioning

confidence: 99%

“…For the room occupancy detection, PIR sensors are usually less susceptible to false-ons, but sometimes switch off services when the space is still occupied; so, incorporating other sensing technologies with a PIR sensor may help to reduce false-offs [3]. In [3] and [4], the authors used acoustic sensor (microphone) to complement PIR sensors, the so called dual-technology sensor, in order to enhance accuracy. Generally, sensor readings are uncertain because internal and external sources add noise to the readings or cause a malfunction of the sensor, altogether.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Sensor Fusion for Indoor Occupancy Determination

Hsiao

Lin

et al. 2015

AMM

Self Cite

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Pyroelectric infrared (PIR) sensors can detect the presence of human without the need to carry any device, which are widely used for human presence detection in home/office automation systems in order to improve energy efficiency. However, PIR detection is based on the movement of occupants. For occupancy detection, PIR sensors have inherent limitation when occupants remain relatively still. Multisensor fusion technology takes advantage of redundant, complementary, or more timely information from different modal sensors, which is considered an effective approach for solving the uncertainty and unreliability problems of sensing. In this paper, we proposed a simple multimodal sensor fusion algorithm, which is very suitable to be manipulated by the sensor nodes of wireless sensor networks. The inference algorithm was evaluated for the sensor detection accuracy and compared to the multisensor fusion using dynamic Bayesian networks. The experimental results showed that a detection accuracy of 97% in room occupancy can be achieved. The accuracy of occupancy detection is very close to that of the dynamic Bayesian networks.

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A Robust Occupancy-Based Building Lighting Framework Using Wireless Sensor Networks

Cited by 4 publications

References 8 publications

Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system

Enabling Smart Air Conditioning by Sensor Development: A Review

Multimodal Sensor Fusion for Indoor Occupancy Determination

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