Methods for Predicting Failures in a Smart Home

Zhebka, V. та Skladannyi, Pavlo та Bazak, Y та Bondarchuk, A. та Storchak, K. (2024) Methods for Predicting Failures in a Smart Home Digital Economy Concepts and Technologies Workshop 2024, 3665. с. 70-78. ISSN 1613-0073

Текст V_Zhebka_P_Skladannyi_Y_Bazak_A_Bondarchuk_K_Storchak_DECaT2024_3665.pdf Download (693kB)

Анотація

Methods for predicting possible failures in smart home systems and analyzing the data required for this have been considered in the study. A study of machine learning methods has been carried out: their features, advantages, and disadvantages have been identified, the metrics of each method have been studied, and the effectiveness of methods for predicting failures in a smart home has been established. It has been found that the Long Short-Term Memory (LSTM) model is distinguished by its ability to work with data sequences and store information for a long time. The characteristics of the LSTM method and its algorithm have been studied in detail. The study emphasizes the importance of collecting and processing various data, such as sensor data, energy consumption, and information about devices and users. The results of the study can be useful for the further development of smart home control systems to improve their reliability and efficiency.

Тип елементу :	Стаття
Ключові слова:	Long short-term memory; LSTM; Machine learning; data processing; forecasting; smart home; failure; information technology
Типологія:	Статті у базах даних > Scopus
Підрозділи:	Факультет інформаційних технологій та математики > Кафедра інформаційної та кібернетичної безпеки ім. професора Володимира Бурячка
Користувач, що депонує:	Павло Миколайович Складанний
Дата внесення:	09 Лип 2024 13:47
Останні зміни:	09 Лип 2024 13:47
URI:	https://elibrary.kubg.edu.ua/id/eprint/48728

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