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.

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 Performance Assessment for Short-Term Water Demand Forecasting Models on Distinctive Water Uses in Korea

2021 ◽  
Vol 13 (11) ◽  
pp. 6056
Author(s):  
Kang-Min Koo ◽  
Kuk-Heon Han ◽  
Kyung-Soo Jun ◽  
Gyu-Min Lee ◽  
Jung-Sik Kim ◽  
...  
Keyword(s):  
Water Supply ◽  
Water Consumption ◽  
Water Demand ◽  
Demand Forecasting ◽  
Supply System ◽  
Water Supply System ◽  
Smart Meter ◽  
Short Term ◽  
Water Demand Forecasting ◽  
Smart Water

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real-time through a smart meter, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include Autoregressive Integrated Moving Average, Radial Basis Function-Artificial Neural Network, Quantitative Multi-Model Predictor Plus, and Long Short-Term Memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand in the SWG demonstration plant. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from a smart meter, and the performance of each model was assessed. The Smart Water Grid Research Group installed a smart meter in block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the Residual, Root Mean Square Error, Normalized Root Mean Square Error, Nash–Sutcliffe Efficiency, and Pearson Correlation Coefficient as indices. As a result of water demand forecasting, it is difficult to forecast water demand only by time and water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.

scholarly journals Water Demand Forecasting using Deep Learning in IoT Enabled Water Distribution Network

2020 ◽  
Vol 15 (6) ◽  
Author(s):  
Lakshmi Kanthan Narayanan ◽  
Suresh Sankaranarayanan ◽  
Joel J P C Rodrigues ◽  
Sergei Kozlov
Keyword(s):  
Deep Learning ◽  
Water Demand ◽  
Distribution System ◽  
Water Distribution ◽  
Short Term Memory ◽  
Water Distribution System ◽  
Distribution Network ◽  
Demand Forecasting ◽  
Water Distribution Network ◽  
Water Losses

Most of the water losses occur during water distribution in pipelines during transportation. In order to eradicate the losses, an “IoT based water distribution system” integrated with “Fog and Cloud Computing" proposed for water distribution and underground health monitoring of pipes. For developing an effective water distribution system based on Internet of Things (IoT), the demand of the consumer should be analysed. So, towards predicting the water demand for consumers, Deep learning methodology called Long Short-Term Memory (LSTM) is compared with traditional Time Series methodology called Auto Regressive Integrated Moving Average (ARIMA) in terms of error and accuracy. Now based on demand prediction with higher accuracy, an IoT integrated “Water Distribution Network (WDN)” is designed using hydraulic engineering. This WDN design will ensure minimal losses during transportation and quality of water to the consumers. This will lead to development of a smart system for water distribution.

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.

Taxi Booking Mobile App Order Demand Prediction Based on Short-Term Traffic Forecasting

2017 ◽  
Vol 2634 (1) ◽  
pp. 57-68 ◽  
Author(s):  
Yunxuan Li ◽  
Jian Lu ◽  
Lin Zhang ◽  
Yi Zhao
Keyword(s):  
High Frequency ◽  
Demand Forecasting ◽  
Low Frequency ◽  
Original Data ◽  
Mobile App ◽  
Support Vector ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Demand Prediction

The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.

scholarly journals Estimating High Resolution Temporal Scale of Water Demand Time Series – Disaggregation Approach (Case Study)

10.29007/4vfl ◽  
2018 ◽  
Author(s):  
Peyman Yousefi ◽  
Gholamreza Naser ◽  
Hadi Mohammadi
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Water Demand ◽  
Distribution System ◽  
Urban Areas ◽  
Water Distribution ◽  
Demand Forecasting ◽  
Temporal Scale ◽  
A New Technique ◽  
Cascade Method

A comprehensive understanding of water demand and its availability is essential for decision-makers to manage their resources and understand related risks effectively. Historical data play a crucial role in developing an integrated plan for management of water distribution system. The key is to provide high-resolution temporal-scale of demand data in urban areas. In the literature, many studies on water demand forecasting are available; most of them were focused on monthly-scales. Since monitoring of time series is a prolonged and costly procedure, the popularity of disaggregation methods is a most recent desirable trend. The objective of this research is to transfer low-resolution into high-resolution temporal scale using random cascade disaggregation and non-linear deterministic methods. This study defines a new technique to apply previously proposed random cascade method to disaggregate continuous data of the city of Peachland. The accuracy of the results is more than 90%. It represents a satisfactory application of the models. The proposed approach helps operators to have access to daily demand without acquiring high-resolution temporal scale values. Although the disaggregated values may not be precisely equal with observed values, it offers a practical solution for the low equipped WDS and leads to lesser number of drinking water-related problems.

scholarly journals Water-Demand Growth Modelling in Puerto Ayora’s Water Distribution Network Using EPANET

2019 ◽  
pp. 71-81
Author(s):  
María Reyes ◽  
Nemanja Trifunović ◽  
Saroj Sharma ◽  
Maria Kennedy
Keyword(s):  
Water Supply ◽  
Water Demand ◽  
Water Distribution ◽  
Water Distribution Network ◽  
Planning Horizon ◽  
Growth Modelling ◽  
Hydraulic Characteristics ◽  
Demand Growth ◽  
Moderate Growth ◽  
The Town

This paper elaborates the hydraulic characteristics of the water supply network of the town of Puerto Ayora. First, it intends to replicate the household individual storage by simulating nodal tanks with the use of the EPANET software. Later, it uses the Pressure-Driven Approach (PDA) to develop a methodology that estimates the overflow of storage facilities, one of the main sources of wastage in Puerto Ayora. Finally, it uses the Demand-Driven Approach (DDA), with the aim of assessing the network in the future, under four population growth scenarios. With the chosen moderate growth scenario, two options are suggested in order to tackle the water supply issues at the end of the planning horizon.

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