A machine-learning based approach to model user occupancy and activity patterns for energy saving in buildings

Ortega, Jose Luis Gomez; Han, Liangxiu; Whittacker, N.; Bowring, Nicholas

doi:10.1109/sai.2015.7237185

Cited by 35 publications

(16 citation statements)

References 27 publications

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“…An additional function of the system is to monitor the condition of lighting. [13,14,15,16] In the buildings with nearly zero energy consumption, sustainable and equipped with smart technologies, control systems above the standard are often used. These include adaptive regulation that uses information collection and algorithm synthesis based on the current behaviour of a particular system, and fuzzy regulation based on the transformation of information using fuzzy logic [17,18,19].…”

Section: Results Of the Literature Reviewmentioning

confidence: 99%

The Contribution of BMS Application Towards the Sustainability and Life Cycle Costs Optimization of Buildings: A Case of Public Utility Facilities in Poland

Araszkiewicz¹,

Jakubowska²,

Saurav³

2020

Proceedings of the Creative Construction E-Conference 2020

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BMS (Building Management System), i.e. the central building control, assumes integrated control of all building functions-from air conditioning and heating to lighting, access control and security issues. Properly designed BMS solutions improve the operation of the building service, allow for a quick reaction in case of emergency situations and significantly facilitate maintenance activities. Building automation systems also allow for data collection and analysis, which can be used among other things, to optimise energy and water consumption in buildings. The aim of the paper is to provide and to discuss findings of the analysis of the impact of the BMS application in the city stadium design, which is an example of a public facility, on the possibility of controlling the life cycle costs of the facility under examination. In the first step, on the basis of the design documentation and the stadium operation schedule prepared by the authors, the life cycle costs of the analysed facility were calculated in accordance with the methodology recommended by the Polish public procurement law with use of the SMART SPP calculation tool. The functional assumptions of the BMS designed for the stadium were taken into account. Then a change was introduced to the analysis at the input consisting in the simulation of BMS removal from the facility and then the introduction of the risk factor of human error on the part of the staff during the annual operation of the stadium in relation to the operation of the lighting system of the facility. The analysis is summarised by comparing the calculation results for both scenarios. The findings indicate the benefits of using BMS in facility management.

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Section: Results Of the Literature Reviewmentioning

confidence: 99%

The Contribution of BMS Application Towards the Sustainability and Life Cycle Costs Optimization of Buildings: A Case of Public Utility Facilities in Poland

Araszkiewicz¹,

Jakubowska²,

Saurav³

2020

Proceedings of the Creative Construction E-Conference 2020

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“…The features extracted from the data sourced from different sensors are used by these algorithms to detect occupancy. Examples of additional algorithms are discussed in [34] and [35]. In [36], an Autoregressive Hidden Markov Model (ARHMM) was developed to model the occupancy pattern using data from PIR sensors, carbon dioxide, concentration and relative humidity sensors.…”

Section: Algorithms For Occupancy Detectionmentioning

confidence: 99%

A Web-Based Office Climate Control System Using Wireless Sensors

et al. 2016

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Abstract-Conventional heating, ventilating, and air conditioning (HVAC) systems usually achieve the desired control level by means of simple ON-OFF control, which can often result in high energy wastage. A potential solution to this issue is intelligent self-regulating HVAC controllers, which base their actions/decisions on sensor data. In this paper, an office climate monitoring and control system is designed and implemented. The system consists of various wireless sensor nodes and a control node. The sensor nodes provide the sensor data necessary to determine occupancy and the control node executes the algorithm, which decides whether to activate cooling or heating based on the sensor data. This system can serve as a controller and can be integrated into HVAC systems in smart buildings. It is shown that the developed control algorithm executed on the control node results in an improvement of up to 39% in energy efficiency over conventional ON-OFF controllers for HVAC systems.

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“…From the above discussion and Table 1, it is obvious that these existing indoor occupancy detection and counting methods cannot fully meet the rigid requirements of next-generation smart air conditioners (Labeodan et al, 2015). To address these challenges, it is interesting to collaborate on the design of smart building systems and information technology for energy savings in buildings (Ortega et al, 2015;Huang et al, 2017). In this work, we explore the design of next-generation CNN-based visual recognition air conditioner.…”

Section: Introductionmentioning

confidence: 99%

Development of CNN-based visual recognition air conditioner for smart buildings

Huang¹,

Hao²

2020

ITcon

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Demand-driven heating, ventilation, and air conditioning (HVAC) operations have become very attractive in energy-efficient smart buildings. Demand-oriented HVAC control largely relies on accurate detection of building occupancy levels and locations. So far, existing building occupancy detection methods have their disadvantages, and cannot fully meet the expected performance. To address this challenge, this paper proposes a visual recognition method based on convolutional neural networks (CNN), which can intelligently interpret visual contents of surveillance cameras to identify the number of occupants and their locations in buildings. The proposed study can detect the quantity, distance, and angle of indoor human users, which is essential for controlling air-conditioners to adjust the direction and speed of air blow. Compared with the state of the art, the proposed method successfully fulfills the function of building occupant counting, which cannot be realized when using PIR, sound, and carbon dioxide sensors. Our method also achieves higher accuracy in detecting moving or stationary human bodies and can filter out false detections (such as animal pets or moving curtains) that are existed in previous solutions. The proposed idea has been implemented and collaboratively tested with air conditioners in an office environment. The experimental results verify the validity and benefits of our proposed idea.

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A machine-learning based approach to model user occupancy and activity patterns for energy saving in buildings

Cited by 35 publications

References 27 publications

The Contribution of BMS Application Towards the Sustainability and Life Cycle Costs Optimization of Buildings: A Case of Public Utility Facilities in Poland

The Contribution of BMS Application Towards the Sustainability and Life Cycle Costs Optimization of Buildings: A Case of Public Utility Facilities in Poland

A Web-Based Office Climate Control System Using Wireless Sensors

Development of CNN-based visual recognition air conditioner for smart buildings

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