A Short-Term Data Based Water Consumption Prediction Approach

Benítez, Rafael; Ortiz-Caraballo, Carmen; Preciado, Juan Carlos; Conejero, José María; Figueroa, Fernando; Rubio-Largo, Álvaro

doi:10.3390/en12122359

Cited by 20 publications

(6 citation statements)

References 34 publications

(47 reference statements)

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“…According to predictive model quality standards, a MAPE error below 10% should be regarded as a determinant of highly accurate forecasting 58 . Although it is not possible to directly compare the models created by other authors due to the different conditions and the scope of the conducted research, there are a number of results in which the MAPE values of forecasts range from 2.0 to 3.0% 17 , 37 , 50 or they were containing larger error 49 , 57 , 59 . To make the obtained results more reliable, water consumption forecasts were intentionally made using standard methods (LR, SVR, MLP, RF, CART) on analogous data sets, which was described in the previous chapter.…”

Section: Resultsmentioning

confidence: 99%

Improving short-term water demand forecasting using evolutionary algorithms

Stańczyk

Kajewska-Szkudlarek

Lipiński

et al. 2022

Sci Rep

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Modern solutions in water distribution systems are based on monitoring the quality and quantity of drinking water. Identifying the volume of water consumption is the main element of the tools embedded in water demand forecasting (WDF) systems. The crucial element in forecasting is the influence of random factors on the identification of water consumption, which includes, among others, weather conditions and anthropogenic aspects. The paper proposes an approach to forecasting water demand based on a linear regression model combined with evolutionary strategies to extract weekly seasonality and presents its results. A comparison is made between the author's model and solutions such as Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Random Forest (RF). The implemented daily forecasting procedure allowed to minimize the MAPE error to even less than 2% for water consumption at the water supply zone level, that is the District Metered Area (DMA). The conducted research may be implemented as a component of WDF systems in water companies, especially at the stage of data preprocessing with the main goal of improving short-term water demand forecasting.

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Section: Resultsmentioning

confidence: 99%

Improving short-term water demand forecasting using evolutionary algorithms

Stańczyk

Kajewska-Szkudlarek

Lipiński

et al. 2022

Sci Rep

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“…Benítez et al [118] perform a study that applies pattern similarity-based methods to predict water consumption, which they successfully used to detect and locate water leaks in early stages. They conclude that transforming traditional water distribution networks into smart water networks consisting of a large number of devices scattered over the network and measuring a wide variety of parameters of the distribution network in a continuous and automatic manner would support water flow daily predictions.…”

Section: Water Demand Forecastingmentioning

confidence: 99%

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Hângan

Chiru

Arsene

et al. 2022

Water

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Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.

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“…[ 12 ] Based on the model similarity, a short‐term prediction method is proposed to the prediction of urban water demand. [ 13 ] The deep learning method is introduced to make short‐term prediction of water demand, and the results show that this method improves the prediction accuracy of water demand. [ 14 ] Combining the factors such as weather and seasons, four machine learning methods are used to study the real‐time water management of water companies.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Water Consumption in 31 Provinces of China Based on FGM(1,1) Model

Qiao

Yang

2022

CLEAN Soil Air Water

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Human production activities and social development cannot be separated from the indispensable water resources. In recent years, with the increasing speed of China's economic development, the consumption of water resources has gradually become the focus of attention of the Chinese government and the public. Based on the original data of water use of 31 provinces (municipalities and autonomous regions) in China from 2014 to 2018, a fractional order accumulative gray prediction model (FGM(1,1)) is established to predict the water use of 31 provinces (municipalities and autonomous regions) in China in the short term, and the applicability of the model is verified by the mean absolute percentage error (MAPE) value. According to the MAPE value, the data obtained by the FGM(1,1) model is relatively accurate. The prediction results show that in case of little change in total water consumption, agricultural water and industrial water use show a downward trend, while domestic water use shows an upward trend. The government's water management policy for urban residents needs to be adjusted, and the publicity work to improve the water-saving awareness of urban residents needs to be improved.

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A Short-Term Data Based Water Consumption Prediction Approach

Cited by 20 publications

References 34 publications

Improving short-term water demand forecasting using evolutionary algorithms

Improving short-term water demand forecasting using evolutionary algorithms

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Prediction of Water Consumption in 31 Provinces of China Based on FGM(1,1) Model

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