Enabling Smart Air Conditioning by Sensor Development: A Review

Cheng, Chin‐Chi; Lee, Dasheng

doi:10.3390/s16122028

Cited by 30 publications

(27 citation statements)

References 61 publications

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“…Having access to this data, the smart air conditioner is able to adapt the cooling effort. Therefore, well developed devices not only can reduce the environmental impact but also enhance users' comfort [15]. In the interest of comparing the energy savings achieved with an air conditioner with other devices, the percentage of the total energy consumption has to be calculated.…”

Section: Smart Air Conditionermentioning

confidence: 99%

Ranking of Energy Saving Devices for Smart Homes According to Their Payback Time

Felderer

Brandtweiner

Hoeltl

2018

WIT Transactions on Ecology and the Environment

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This paper discusses the average energy savings of various smart devices in connection with their average price. By calculating the devices' payback times, a ranking of the tools can be given. The whole study focuses on the average household within the EU-28 in terms of climate as well as in terms of user behaviour. The purpose of the research was to provide a win-win situation for users' wallets and the environment by showing the device which suits both players best. As a result of the research, it was found that the greatest reduction in energy consumption can be reached by an interaction of the smart device and the inhabitants of a smart home. By giving users feedback on their energy consumption through smart meters, average savings of 7.5% are reached. As a smart meter is available for about € 80, it has a payback time of only 4.24 months.conveniently control home electronics and appliances including audio/video, home office, telecommunications, intercom, security, lighting, HVAC, and lawn sprinklers. Control systems can also provide information -residents can find out how much electricity they've used on specific appliances or systems, and utilities can read meters remotely. The systems can be accessed from remote locations by phone or computer, allowing residents to turn on the heat, for example, on their way home from work." The definition of smart homes by Harper and Aldrich [6] has lost nothing of its actuality. The single devices were developed a lot further and now suit their tasks better, but the overall concept has not changed:"A "smart home" can be defined as a residence equipped with computing and information technology which anticipates and responds to the needs of the occupants, working to promote their comfort, convenience, security and entertainment through the management of technology within the home and connections to the world beyond." Energy saving potentialsA lot of research has been done on the topic of smart homes in general and on its ability to save energy. As presented by Harper and Aldrich [6] the idea of smart homes has a relatively long history. The process of home automation started in about 1915. The introduction of the term "smart" into homes was conducted in the nineties of the last century. According to Louis' paper from 2015 [7], the general energy saving potential of smart systems in Finland is a reduction of 14% of the total energy consumption. He further claims the payback time for smart meters to be 3.5 months. Nevertheless, only little studies can be found which calculate average saving potentials. Most of the studies and whitepapers focus on maximum values. However, a sufficient amount of studies and whitepapers can be found that give an average value of savings in order to calculate the payback times of the various devices. The Nest White Paper published in 2015 [8] gives explicit average savings concerning the smart thermostat and so does for example the study conducted in 2011 by Williams et al. for lighting control systems [9]. Summing up, the field of smart home de...

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Section: Smart Air Conditionermentioning

confidence: 99%

Ranking of Energy Saving Devices for Smart Homes According to Their Payback Time

Felderer

Brandtweiner

Hoeltl

2018

WIT Transactions on Ecology and the Environment

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“…With the development of information and communication technologies such as the computer, cloud, and Internet of Things (IoT), the machine learning technologies for image processing and voice processing have been combined with the deep learning technology and their application fields are spreading throughout the industry [1,2]. Moreover, as energy efficiency regulations are strengthened, increasing the energy efficiency of electronic appliances used in daily life becomes an important task [3,4]. Among electronic appliances in the living space, air conditioners use the most energy, and because of that numerous techniques have been studied to maximize energy efficiency while maintaining the same performance [3,5].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, as energy efficiency regulations are strengthened, increasing the energy efficiency of electronic appliances used in daily life becomes an important task [3,4]. Among electronic appliances in the living space, air conditioners use the most energy, and because of that numerous techniques have been studied to maximize energy efficiency while maintaining the same performance [3,5]. To address these problems, various human body detection sensors are used to detect the presence of humans and control the airflow of the air conditioner [4,6].…”

Section: Introductionmentioning

confidence: 99%

“…To address these problems, various human body detection sensors are used to detect the presence of humans and control the airflow of the air conditioner [4,6]. However, there are several technical problems with detecting a person and control an airflow based on the limited viewing angle of the sensor, with the constrained condition of the installed position of the product and the number of sensors [3,7]. In addition, when the vanes for discharging cold air are fixed, the cold air is concentrated only at a specific region, and cause an uneven temperature variation in the indoor space.…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network Based Ambient Airflow Control through Spatial Learning

2020

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As global energy regulations are strengthened, improving energy efficiency while maintaining performance of electronic appliances is becoming more important. Especially in air conditioning, energy efficiency can be maximized by adaptively controlling the airflow based on detected human locations; however, several limitations such as detection areas, the installation environment, and sensor quantity and real-time performance which come from the constraints in the embedded system make it a challenging problem. In this study, by using a low resolution cost effective vision sensor, the environmental information of living spaces and the real-time locations of humans are learned through a deep learning algorithm to identify the living area from the entire indoor space. Based on this information, we improve the performance and the energy efficiency of air conditioner by smartly controlling the airflow on the identified living area. In experiments, our deep learning based spatial classification algorithm shows error less than ± 5 ° . In addition, the target temperature can be reached 19.8% faster and the power consumption can be saved up to 20.5% by the time the target temperature is achieved.

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“…A literature review byCheng and Lee (2016) showed that the energy-savings potential of ACs improved from 11% before 2000 to 30% after 2000 owing to advancements in sensor technologies.…”

mentioning

confidence: 99%

Challenges and Recommended Policies for Simultaneous Global Implementation of Low-GWP Refrigerants and High Efficiency in Room Air Conditioners

Park

Shah

Ding

et al. 2019

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Increasing incomes, electrification, and urbanization-as well as a warming world-are driving up the global stock of air conditioners (ACs), particularly in emerging economies with hot climates. AC energy consumption is expected to increase substantially as the global stock of room ACs rises to 1.5 billion in 2030 and 2.5 billion in 2050. Hence, improving AC energy efficiency will be critical to reducing AC energy, cost (consumer lifecycle cost, electricity generation cost, etc.), peak load, and emissions impacts. The 2016 Kigali Amendment to the Montreal Protocol offers an opportunity to improve AC energy efficiency in tandem with the phasedown of high global warming potential (GWP) hydrofluorocarbon (HFC) refrigerants. Based on the most recent information, a literature review, and interviews with manufacturers and industry experts, we find the main barriers to deploying high-efficiency ACs include concerns about market demand and cost, which could be mitigated by appropriately improved design of market-transformation programs such as standards and labeling, incentive, and procurement programs. The main barriers to the low-GWP refrigerant transition include the need for timely revision of safety standards and associated costs for capacity-building activities allowing safe use of low-GWP refrigerants in ACs. Policy action and the market transformation can be accelerated by advancing the refrigerant transition and efficiency improvements in parallel.

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Enabling Smart Air Conditioning by Sensor Development: A Review

Cited by 30 publications

References 61 publications

Ranking of Energy Saving Devices for Smart Homes According to Their Payback Time

Ranking of Energy Saving Devices for Smart Homes According to Their Payback Time

Deep Neural Network Based Ambient Airflow Control through Spatial Learning

Challenges and Recommended Policies for Simultaneous Global Implementation of Low-GWP Refrigerants and High Efficiency in Room Air Conditioners

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