Abstract
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.
Highlights
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]
To evaluate the performance of the living area detection with the deep learning based spatial learning algorithm, an air conditioner is fixed in the space as shown in Figure 14, and the time taken to identify the living area is measured while changing the area (a ∼ b)
Improving energy efficiency is becoming an important issue for all electronic appliances, especially with the strengthening of global energy regulations
Summary
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]. 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]. 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 It causes a feeling of discomfort when the cool air directly affects the people in the living area [3,5]. Our human body detection algorithm can operate effectively even on an embedded system with very low computing power with a cheap and a low resolution camera mounted on an air conditioner
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