Multiple perspectives of the value of occupancy-based HVAC control systems

Nikdel, Leila; Janoyan, Kerop D.; Bird, Stephen; Powers, Susan E.

doi:10.1016/j.buildenv.2017.11.039

Cited by 57 publications

(14 citation statements)

References 25 publications

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“…Moreover, the satisfaction-based control reduced consumption by about 15.3% and 11.9% in these test-beds. In a small office building, for five climate zones representative of the United States, Nikdel et al [41] found that occupancy-based strategies could bring 22-50% and 47-87% reduction in electricity and natural gas use, respectively, compared to no thermostat control. Considering the response of 1400 thermal comfort surveys in a naturally ventilated building, Yongchao et al [42] found that with adaptive control behaviors, occupants were thermally neutral and satisfied from 18 to 27 • C. Their satisfaction exceeded that predicted by ASHRAE Standard 55 or PMV-based ISO standards.…”

Section: Thermal Comfort: Indoor Plant Emitters and Control Strategiesmentioning

confidence: 99%

Experimental Comparison of Heating Emitters in Mediterranean Climate

2021

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The need to increase the level of quality of indoor environments requires an extremely accurate definition of the microclimatic requisites to guarantee, in the spaces where people live and work, global and local conditions of comfort, considering, at the same time, the aspects related to energy savings and environmental sustainability. In this framework, the paper proposes a comparison of indoor parameters for three different types of heating emitters: fan-coils, baseboards heaters, and radiant floor systems. The comparison is based on seasonal monitoring performed in a test-room located in a Mediterranean climate; it can simulate an insulated room with office usage. The proposed indices demonstrate that the floor radiant system is characterized by lower horizontal and vertical differences in air temperature distribution that can guarantee more comfortable conditions and lower heat losses. The operative temperature is often higher than the neutral point; thus, management with a lower set-point temperature should be experimented with in further studies. More generally, the introduced method could help designers to choose the proper system and management strategy with the dual purpose to select a comfortable but energy savings-oriented operating temperature.

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Section: Thermal Comfort: Indoor Plant Emitters and Control Strategiesmentioning

confidence: 99%

Experimental Comparison of Heating Emitters in Mediterranean Climate

2021

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“…Occupancy estimation is an important enabler of various applications such as HVAC (heating, ventilation, and air-conditioning) controlling [12,[15][16][17] and social interaction [18]. For example, Nikdel et al [15] quantified the energy consumption using building occupancy information. Aftab et al [12] predicted occupancy from video sensing using object-tracking techniques from a scene and controlling the HVAC system in real time.…”

Section: Speaker Sensingmentioning

confidence: 99%

Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments

Khan

Roy

Hossain

2018

Mobile Information Systems

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Occupancy detection helps enable various emerging smart environment applications ranging from opportunistic HVAC (heating, ventilation, and air-conditioning) control, effective meeting management, healthy social gathering, and public event planning and organization. Ubiquitous availability of smartphones and wearable sensors with the users for almost 24 hours helps revitalize a multitude of novel applications. The inbuilt microphone sensor in smartphones plays as an inevitable enabler to help detect the number of people conversing with each other in an event or gathering. A large number of other sensors such as accelerometer and gyroscope help count the number of people based on other signals such as locomotive motion. In this work, we propose multimodal data fusion and deep learning approach relying on the smartphone’s microphone and accelerometer sensors to estimate occupancy. We first demonstrate a novel speaker estimation algorithm for people counting and extend the proposed model using deep nets for handling large-scale fluid scenarios with unlabeled acoustic signals. We augment our occupancy detection model with a magnetometer-dependent fingerprinting-based localization scheme to assimilate the volume of location-specific gathering. We also propose crowdsourcing techniques to annotate the semantic location of the occupant. We evaluate our approach in different contexts: conversational, silence, and mixed scenarios in the presence of 10 people. Our experimental results on real-life data traces in natural settings show that our cross-modal approach can achieve approximately 0.53 error count distance for occupancy detection accuracy on average.

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“…Nikdel et al in small office building models based on American society of heating, refrigerating and air-conditioning engineers (ASHRAE) standard, investigates constant temperature, programmable thermostat and setpoint temperature based on the existence of at least one occupant merged with occupancy scheduled in five different climates zones. Results elucidated that occupancy-based scenarios consume minimal energy than the remaining scenarios [18]. Accordingly, most of the strategies followed to reduce the cooling energy consumed in buildings, considered to be fixed, do not interact with changes within the building environment, and often lack the combination of both temperature setpoint and occupancy information.…”

Section: Introductionmentioning

confidence: 99%

An occupancy-based strategy employing computer vision for reducing cooling energy consumed in buildings

2021

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The energy expended to cool the occupied areas by air conditioners represents a substantial share of the total energy exhausted in buildings. Therefore, developing strategies to reduce this energy is crucial. One of the preponderance strategies adopted to depreciate energy consumption in buildings is the occupancy-based strategy. In this research, an innovative model was established to achieve the goal of reducing cooling energy consumed in buildings based on occupancy-based combined with a constant temperature setpoint strategy in two phases, and each phase engrosses in 20 days. Phase one is to identify the extent of cooling energy employed according to the use of room occupants and its costs in consumption was 276.01 kWh after completion of this phase. Sequentially, constructing phase two intended to reduce cooling energy consumption by employing an automatic air-conditioner (AC) control strategy relying on an improved human detection algorithm with a 25℃ as temperature setpoint, resulting in 112.45 kWh of consumption. To complement the motives for elaboration, the human detection measurement using you only look once (YOLO) improved by applying pre-processing algorithms to reach an average human detection enhancement of 21.2%. The proposed model results showed that potential savings associated with the embraced strategy decreases by more than anticipated as the amount of reduced energy reached 59% savings.

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Multiple perspectives of the value of occupancy-based HVAC control systems

Cited by 57 publications

References 25 publications

Experimental Comparison of Heating Emitters in Mediterranean Climate

Experimental Comparison of Heating Emitters in Mediterranean Climate

Wearable Sensor-Based Location-Specific Occupancy Detection in Smart Environments

An occupancy-based strategy employing computer vision for reducing cooling energy consumed in buildings

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