Abstract
In this study, the authors aimed to realize a smart home using an AI model that can be integrated with the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) application to realize environment control. The collected input data were outdoor temperature, indoor temperature, humidity, illumination, and indoor person count. The output control decisions included control of air conditioners, dehumidifiers, power curtains, and lights. An artificial neural network was utilized to process the input data for machine learning for the objective of achieving a comfortable environment. In addition, the control decision predictions made by this AI model were analyzed for model loss and model accuracy. This study implemented the model. Specifically, LabVIEW was used to design the sensing component, data display, and control interface, and Python was used to establish the intelligent model. Moreover, by using the web publishing tool built into LabVIEW, remote sensing and control were fulfilled in this implementation.
Highlights
Common household items can be transformed into smart devices through sensors. is idea inspires the use of AI models to overcome the problems associated with controlling various appliances in smart homes
The user interface design for integrating an AI data sensing and prediction model for smart home environment control was constructed using Laboratory Virtual Instrument Engineering Workbench (LabVIEW). e monitoring and control interface is shown in Figure 4. e human–machine interface design includes a (A) user login block, (B) sensor data block, (C) environmental monitoring block, (D) environmental prediction block, and (E) AI mode selection block
The LabVIEW Python node that is embedded in LabVIEW is connected to the AI model that must be trained and tested for predicting actual control commands in the household environment
Summary
Common household items can be transformed into smart devices through sensors. is idea inspires the use of AI models to overcome the problems associated with controlling various appliances in smart homes. It remains difficult to manage and control these appliances, while fulfilling households’ needs for comfort, health, energy reduction, and security, through, for example, automatic temperature control and security over the control of devices When such appliances use artificial intelligence to realize remote and environmentally aware automated control, user comfort is improved significantly [1,2,3]. A few scientific works [4, 5] have described the development of “smart homes” through machine learning technologies, and some of the practical implementations of this idea, such as that by Salhi, use machine learning algorithms to realize the early detection of gas leakage and for the control of appliances in smart homes. E proposed deployment can be used to develop AI applications pertaining to data sensing and prediction for controlling smart home environments
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