Connectivity and integration simplicity are two of the main features of the IoT market which drive the rising popularity of Smart Home in the last years. Older technologies like KNX are struggling to follow the rapid development and to deliver the same user experience as IoT giants do. The biggest barrier for the KNX market lies in its configuration specifics, which is not aware of end-customer devices included in the home but only saves the configuration of each functionality. In this master's thesis we developed a component, which reconstructs the complete home configuration into a form ready for visualization or voice control of specific devices based on KNX project file, which includes a complete configuration of KNX Smart Home. In the first phase we prepared a dataset of KNX functions, which are distributed into 65 classes. Using natural language processing techniques and multi-class classification algorithms, we then constructed a prediction model for predicting specific function class based on English and German short text function descriptions. Using this information and other parameters from KNX project file we then group functions into meaningful devices included in the home. At the end, we developed an application module, which includes an element for uploading KNX project file based on which we then generate the adequate configuration. Comparison between the implemented module and the alternative solution showed that we have successfully increased the number of correctly detected devices.
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