Abstract
Different types of sensors along the distribution pipelines are continuously measuring different parameters in Smart WAter Networks (SWAN). The huge amount of data generated contain measurements such as flow or pressure. Applying suitable algorithms to these data can warn about the possibility of leakage within the distribution network as soon as the data are gathered. Currently, the algorithms that deal with this problem are the result of numerous short-term water demand forecasting (WDF) approaches. However, in general, these WDF approaches share two shortcomings. The first one is that they provide low-frequency predictions. That is, most of them only provide predictions with 1-hour time steps, and only a few provide predictions with 15 min time steps. The second one is that most of them require estimating the annual seasonality or taking into account not only data about water demand but also about other factors, such as weather data, that make their use more complicated. To overcome these weaknesses, this work presents an approach to forecast the water demand based on pattern recognition and pattern-similarity techniques. The approach has a twofold contribution. Firstly, the predictions are provided with 1 min time steps within a time lead of 24 hours. Secondly, the laborious estimation of annual seasonality or the addition of other factors, such as weather data, is not needed. The paper also presents the promising results obtained after applying the approach for water demand forecasting to a real project for the detection and location of water leakages.
Highlights
Different types of sensors along the distribution pipelines are continuously measuring different parameters in Smart WAter Networks (SWAN). e huge amount of data generated contain measurements such as flow or pressure
Since the number of data predicted for each day was high (1440 values) and because during an important fraction of the day, the water consumption values were very low, and the value of the mean average percentage error (MAPE) could become very high; for these special cases, we considered the root mean squared error (RMSE) to present a more adequate value to determine the goodness of the prediction
Works is paper has presented an approach based on patternsimilarity techniques to forecast water demand. is work faces two important challenges that have been traditionally neglected in previous approaches, namely, a high frequency of predictions and the need for external data such as annual seasonality or weather that increments the complexity of the approaches
Summary
Different types of sensors along the distribution pipelines are continuously measuring different parameters in Smart WAter Networks (SWAN). e huge amount of data generated contain measurements such as flow or pressure. E second one is that most of them require estimating the annual seasonality or taking into account data about water demand and about other factors, such as weather data, that make their use more complicated To overcome these weaknesses, this work presents an approach to forecast the water demand based on pattern recognition and pattern-similarity techniques. One of them is the problem of water pressure that could affect significantly the level of service for the users and where there are novel approaches such as [7] that proposes the division of the network in subregions according to the expected water peak demand Another huge problem managing WDS is to deal with water loss. It is estimated that the amount of water in the world that is lost is more than 30 percent of production [8]
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