Objectives This paper introduces a predictable smart home which was integrated with cloud computing. A Hakka pinyin input method for Android mobile/cell phones has been provided. Our system achieved the goals of energy saving and carbon reduction, and can be employed in medical care applications, such as long-term care.
Methods This system provides a diversified smart home interface. By mining power consumption data, we can predict people’s future behavior for controlling home appliances. In this paper, we use an n-gram language model for training an (n-1)th-order Markov model. Based on statistical models, we can predict the behavior of residents and patients, and achieve energy saving and carbon reduction. In our system, mobile/cell phone and smart watch apps were developed to provide residents or patients with a convenient interface to control IR appliances, ZigBee appliances and appliances with a WiFi smart plug. The power consumption data must first be obtained by calling the WiFi smart plug’s open API. The open API’s power consumption data is presented in JSON format, as show below:
The attribute of the result is present calling status. The outlet id is the WiFi smart plug’s id. Data is the power consumption string which is shown as 2_Current A, 1/100 = 0.01 A for this case. This should be divided by 100. The home appliance condition should then be determined based on the current information, and the following rule used to generate one string for the present condition at different times. In the rule, the string consists of two parts: the prefix and the time index. In this system, the Hakka pinyin input method for Android mobile/cell phones that enable users to input Hakka words quickly and conveniently has been provided. It is toneless while the user inputs the pinyin, and several response messages for typing errors are included. The smart home apps on mobile/cell phones employ a socket client connecting to the cloud server, allowing residents to use their smart devices and web applications anywhere with an internet connection to control appliances.
Results The experimental data were generated from six appliances generating 4320 sequential states over 1 month. Several experiments were conducted using three n-gram models on six home appliances. A prediction precision of 95.7% on average was achieved for outside testing. We furthermore compare performance and analyze various features to promote the results.
Conclusions A predictable smart home interface integrated with cloud computing and long-term care is implemented using mobile/cell phone apps and various applications. The predictable models for controlling appliances are based on Markov technology. The best prediction rate for outside testing is 95.7% on average. The Hakka pinyin input method for Android mobile/cell phones that enables users to input Hakka words has been provided. Our system achieved the goal of energy saving and carbon reduction, and can be employed for medical care applications in patient’s rooms, for instance for patients in long-term care.
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