20.500.12556/RUP-22592 EcoShower estimating shower duration using non-intrusive multi-modal sensor data via LSTM and Gated Transformer models This paper tackles the challenge of accurately estimating shower duration from non-intrusive multi-modal sensor data to facilitate efficient water management. Efficient water usage is a critical environmental challenge, and showering contributes significantly to domestic water consumption. Developing accurate, accessible monitoring solutions is essential for promoting sustainability. Utilizing data from humidity, temperature, sound average, and sound peak sensors, we explore suitable data processing steps and the application of machine learning models to estimate shower duration. Our approach includes the design of a bidirectional Long Short- Term Memory model and the application of an existing Gated Transformer Network model to address the multivariate time series classification task. Our analysis reveals that both models are highly effective in this context, also compared to baseline models, and humidity emerges as a particularly powerful predictor either on its own or when combined with the temperature sensor. This work not only showcases the potential of using machine learning methods for multivariate time series classification in the domain of water consumption but also underscores the implications for adopting such technologies in promoting sustainable water use. ohranjanje vodnih virov omrežje s preklopnim transformatorjem večmodalni senzorski podatki klasifikacija časovnih vrst dvosmerni dolgoročno-kratkoročni spomin true false false Angleški jezik Slovenski jezik Članek v reviji 2026-02-02 13:56:03 2026-02-02 13:56:05 2026-02-03 03:05:21 0000-00-00 00:00:00 2025 0 0 str. 1-14 article 127202 Vol. 277 5 Jun. 2025 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 0000-00-00 0000-00-00 2025-03-17 544.272 0957-4174 10.1016/j.eswa.2025.127202 237397763 RAZ_Sablica_Lukas_2025.pdf RAZ_Sablica_Lukas_2025.pdf 1 97D48654AE569D824A7BBE0F607353E0 63e3aeb51b8355989b495ac76e5e09d433b86a47e0c48d428cabf902a58b3743 c3bb6697-0036-11f1-b092-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=32871 https://www.sciencedirect.com/science/article/pii/S0957417425008243?via%3Dihub 1 9120726f-0036-11f1-b092-005056ac49c0 https://repozitorij.upr.si/Dokument.php?lang=slv&id=32870 Fakulteta za turistične študije - Turistica 0 0 0