scholarly journals An Innovative Hourly Water Demand Forecasting Preprocessing Framework with Local Outlier Correction and Adaptive Decomposition Techniques

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 582 ◽  
Author(s):  
Shiyuan Hu ◽  
Jinliang Gao ◽  
Dan Zhong ◽  
Liqun Deng ◽  
Chenhao Ou ◽  
...  
Keyword(s):  
Water Demand ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Cost Effective ◽  
Distribution Networks ◽  
Correction Method ◽  
Support Vector ◽  
Forecasting Models ◽  
Water Demand Forecasting ◽  
Local Outlier

Accurate forecasting of hourly water demand is essential for effective and sustainable operation, and the cost-effective management of water distribution networks. Unlike monthly or yearly water demand, hourly water demand has more fluctuations and is easily affected by short-term abnormal events. An effective preprocessing method is needed to capture the hourly water demand patterns and eliminate the interference of abnormal data. In this study, an innovative preprocessing framework, including a novel local outlier detection and correction method Isolation Forest (IF), an adaptive signal decomposition technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and basic forecasting models have been developed. In order to compare a promising deep learning method Gated Recurrent Unit (GRU) as a basic forecasting model with the conventional forecasting models, Support Vector Regression (SVR) and Artificial Neural Network (ANN) have been used. The results show that the proposed hybrid method can utilize the complementary advantages of the preprocessing methods to improve the accuracy of the forecasting models. The root-mean-square error of the SVR, ANN, and GRU models has been reduced by 57.5%, 27.8%, and 30.0%, respectively. Further, the GRU-based models developed in this study are superior to the other models, and the IF-CEEMDAN-GRU model has the highest accuracy. Hence, it is promising that this preprocessing framework can improve the performance of the water demand forecasting models.

scholarly journals Improving the Performance of Water Demand Forecasting Models by Using Weather Input

2014 ◽  
Vol 70 ◽  
pp. 93-102 ◽  
Author(s):  
M. Bakker ◽  
H. van Duist ◽  
K. van Schagen ◽  
J. Vreeburg ◽  
L. Rietveld
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Forecasting Models ◽  
Water Demand Forecasting

A Comparison of Short-Term Water Demand Forecasting Models

2019 ◽  
Vol 33 (4) ◽  
pp. 1481-1497 ◽  
Author(s):  
E. Pacchin ◽  
F. Gagliardi ◽  
S. Alvisi ◽  
M. Franchini
Keyword(s):  
Water Demand ◽  
Demand Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Water Demand Forecasting

scholarly journals A Short-Term Data Based Water Consumption Prediction Approach

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2359 ◽  
Author(s):  
Rafael Benítez ◽  
Carmen Ortiz-Caraballo ◽  
Juan Carlos Preciado ◽  
José M. Conejero ◽  
Fernando Sánchez Figueroa ◽  
...  
Keyword(s):  
Water Demand ◽  
Early Stage ◽  
Demand Forecasting ◽  
Distribution Networks ◽  
Water Demand Forecasting ◽  
Smart Water ◽  
Wide Range ◽  
Pattern Similarity ◽  
Flow Pressure ◽  
Prediction Approach

A smart water network consists of a large number of devices that measure a wide range of parameters present in distribution networks in an automatic and continuous way. Among these data, you can find the flow, pressure, or totalizer measurements that, when processed with appropriate algorithms, allow for leakage detection at an early stage. These algorithms are mainly based on water demand forecasting. Different approaches for the prediction of water demand are available in the literature. Although they present successful results at different levels, they have two main drawbacks: the inclusion of several seasonalities is quite cumbersome, and the fitting horizons are not very large. With the aim of solving these problems, we present the application of pattern similarity-based techniques to the water demand forecasting problem. The use of these techniques removes the need to determine the annual seasonality and, at the same time, extends the horizon of prediction to 24 h. The algorithm has been tested in the context of a real project for the detection and location of leaks at an early stage by means of demand forecasting, and good results were obtained, which are also presented in this paper.

scholarly journals IoT-based water demand forecasting and distribution design for smart city

2019 ◽  
Vol 11 (4) ◽  
pp. 1411-1428 ◽  
Author(s):  
Lakshmi Kanthan Narayanan ◽  
Suresh Sankaranarayanan
Keyword(s):  
Water Demand ◽  
Distribution System ◽  
Smart City ◽  
Water Distribution ◽  
Water Distribution System ◽  
Moving Average ◽  
Demand Forecasting ◽  
Underground Water ◽  
Daily Basis ◽  
Water Demand Forecasting

Abstract The percentage of fresh water resource availability in the world is diminishing every year. According to a world economic forum survey, the increase in water demand will result in high scarcity globally in the next two decades. The eradication of the water demand increase and reducing the losses during the transportation of water is challenging. Thus accordingly, an Internet of Things (IoT)-based architecture integrated with Fog for underground water distribution system has been proposed. Towards designing an IoT water distribution architecture for a smart city, we need to first forecast the water demand for consumers. Hence, accordingly, water demand forecasting has been carried out on a daily basis for a period of three months as a case study using autoregressive integrated moving average (ARIMA) and regression analysis. Based on water demand forecasting analysis, a water distribution design for an IoT-based architecture has been carried out using hydraulic engineering design for proper distribution of water with minimal losses which would result in the development of a smart water distribution system (SWDS). This has been carried out using EPANET.

scholarly journals Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques

Resources ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 156 ◽  
Author(s):  
Oluwaseun Oyebode ◽  
Desmond Eseoghene Ighravwe
Keyword(s):  
Soft Computing ◽  
Predictive Models ◽  
Water Demand ◽  
Demand Forecasting ◽  
Support Vector ◽  
Ann Model ◽  
Real World Data ◽  
Water Demand Forecasting ◽  
Soft Computing Techniques ◽  
Computing Models

Previous studies have shown that soft computing models are excellent predictive models for demand management problems. However, their applications in solving water demand forecasting problems have been scantily reported. In this study, feedforward artificial neural networks (ANNs) and a support vector machine (SVM) were used to forecast water consumption. Two ANN models were trained using different algorithms: differential evolution (DE) and conjugate gradient (CG). The performance of these soft computing models was investigated with real-world data sets from the City of Ekurhuleni, South Africa, and compared with conventionally used exponential smoothing (ES) and multiple linear regression (MLR). The results obtained showed that the ANN model that was trained with DE performed better than the CG-trained ANN and other predictive models (SVM, ES and MLR). This observation further demonstrates the robustness of evolutionary computation techniques amongst soft computing techniques.

scholarly journals Correlation Analysis of Water Demand and Predictive Variables for Short-Term Forecasting Models

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
B. M. Brentan ◽  
G. Meirelles ◽  
M. Herrera ◽  
E. Luvizotto ◽  
J. Izquierdo
Keyword(s):  
Water Demand ◽  
Distribution Systems ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Principal Component ◽  
Correlation Study ◽  
Self Organizing Maps ◽  
Water Demand Forecasting ◽  
Predictive Variables ◽  
Short Term Forecasting

Operational and economic aspects of water distribution make water demand forecasting paramount for water distribution systems (WDSs) management. However, water demand introduces high levels of uncertainty in WDS hydraulic models. As a result, there is growing interest in developing accurate methodologies for water demand forecasting. Several mathematical models can serve this purpose. One crucial aspect is the use of suitable predictive variables. The most used predictive variables involve weather and social aspects. To improve the interrelation knowledge between water demand and various predictive variables, this study applies three algorithms, namely, classical Principal Component Analysis (PCA) and machine learning powerful algorithms such as Self-Organizing Maps (SOMs) and Random Forest (RF). We show that these last algorithms help corroborate the results found by PCA, while they are able to unveil hidden features for PCA, due to their ability to cope with nonlinearities. This paper presents a correlation study of three district metered areas (DMAs) from Franca, a Brazilian city, exploring weather and social variables to improve the knowledge of residential demand for water. For the three DMAs, temperature, relative humidity, and hour of the day appear to be the most important predictive variables to build an accurate regression model.

scholarly journals Short-term water demand forecasting using machine learning techniques

2018 ◽  
Vol 20 (6) ◽  
pp. 1343-1366 ◽  
Author(s):  
A. Antunes ◽  
A. Andrade-Campos ◽  
A. Sardinha-Lourenço ◽  
M. S. Oliveira
Keyword(s):  
Machine Learning ◽  
Water Demand ◽  
Demand Forecasting ◽  
Maximum Level ◽  
Water Utilities ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Learning Techniques

Abstract Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

scholarly journals Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection

Water ◽  
2017 ◽  
Vol 9 (3) ◽  
pp. 224 ◽  
Author(s):  
Antonio Candelieri
Keyword(s):  
Water Demand ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Water Distribution Network ◽  
Data Driven ◽  
Support Vector ◽  
Smart Meter ◽  
Short Term ◽  
First Case ◽  
Two Stages

This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data) and individual customer water consumption (AMR data). In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.

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