Abstract
This article presents a real-time data analysis platform to forecast water consumption with Machine-Learning (ML) techniques. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption. This monitoring is carried out through a communicating system for collecting data in the form of unevenly spaced time series. The platform is completed by learning capabilities to analyze and forecast water consumption. The analysis consists of checking the data integrity and inconsistency, in looking for missing data, and in detecting abnormal consumption. Forecasting is based on the Long Short-Term Memory (LSTM) and the Back-Propagation Neural Network (BPNN). After evaluation, results show that the ML approaches can predict water consumption without having prior knowledge about the data and the users. The LSTM approach, by being able to grab the long-term dependencies between time steps of water consumption, allows the prediction of the amount of consumed water in the next hour with an error of some liters and the instants of the 5 next consumed liters in some milliseconds.
Highlights
Water consumption analysis is crucial as it assists building managers and operators to adopt better strategies to plan usages [1]
We propose a framework based on ML algorithms such as the Long Short-Term Memory (LSTM) [8] and the Back-Propagation
We presented a web-oriented platform to collect in real-time water consumption data and to predict them with machine-learning approaches
Summary
Water consumption analysis is crucial as it assists building managers and operators to adopt better strategies to plan usages [1]. The strategy fully relies on a web-oriented architecture to ensure better management and optimized monitoring of water consumption [5]. With implicitly sampled time series, the ML approaches predict the instants when the liters will be consumed. Both cases are achieved using the LSTM [8,11] and the BPNN [12]. Consumption data are presented in terms of water volumes, indexes and dates of events In other words, these data are considered to be unevenly spaced time series or Load Curves (LC).
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