A hybrid model of machine learning for classifying household water-consumption behaviors

Abstract

Classifying household water-consumption behaviors is crucial for providing targeted suggestions for water-saving behaviors and enabling effective resource management and conservation. Although it is common knowledge that energy consumption is closely coupled with household water consumption, the effectiveness of energy consumption information in classifying household water-consumption behaviors remains unexplored. This study proposes a hybrid model of long short-term memory (LSTM) and random forest (RF) using water and electricity consumption as inputs to classify household water-consumption behaviors. Data from three households in Beijing collected from January to March 2020 were used for the case studies. The hybrid model achieved a macro F1 score of 0.89 at a 5-min resolution, outperforming the standalone LSTM and RF models. Additionally, the inclusivity of time-series electricity consumption improves the accuracy (F1 scores) of classifying bathing and laundry behaviors by 0.12 and 0.20, respectively. These findings underscore the scientific value of integrating electricity consumption as a proxy variable in water-consumption behavior classification models, demonstrating its potential to enhance accuracy while simplifying data acquisition processes. This study establishes a framework for demand-side water management aimed at empowering residents to understand their own water-energy consumption behavior patterns and engage in personalized water conservation efforts.