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. • Accurate shower duration estimation using non-intrusive sensor data. • Bidirectional LSTM and GTN models applied to multivariate time series data. • Effective sensor combinations identified for efficient water monitoring. • Potential for promoting sustainable water use with machine learning.