Decision Support Strategies for Household Water Consumption Behaviors Based on Advanced Recommender Systems

Abstract

Water is one of the most important factors that can influence human health. Therefore, constant monitoring of water consumption is essential to maintain a balance of water demand. A recommendation system represents a major challenge, but with huge potential for the water industry, providing consumers the most efficient ways to conserve water based on their data collected from smart water meters. This paper proposes a novel recommendation system design architecture that promotes water conservation behavior among residential consumers from urban areas. We analyzed 480,000 data samples from several households with different profiles to generate personalized recommendations for each household and encourage consumers to adopt measures to raise awareness and reduce water consumption. Moreover, data were collected from three different measurement points in the household (cold_sink, hot_sink, and toilet), with a sampling time of 60 s. The proposed recommendation system implements collaborative filtering combined with a set of rules to generate recommendations based on the consumption patterns of similar households. The results are promising, offering personalized feedback that could help change the consumption behavior of households if the recommendations made are followed.