Adaptive water demand forecasting for near real-time management of smart water distribution systems

This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24 h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand tim...

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Bibliographic Details
Published in:Environmental modelling & software : with environment data news Vol. 60; pp. 265 - 276
Main Authors: Romano, Michele, Kapelan, Zoran
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.10.2014
Elsevier
Subjects:
Adaptive systems
Animal, plant and microbial ecology
Artificial Neural Network
Biological and medical sciences
computer software
Demand
Demand analysis
engineering
environmental models
Evolutionary Algorithm
Forecasting
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Management
Mathematical models
Methodology
Methods and techniques (sampling, tagging, trapping, modelling...)
neural networks
Real time
time series analysis
United Kingdom
water analysis
Water Demand Forecasting
water distribution
British Isles
United Kingdom
Evolutionary Algorithm
Water Demand Forecasting
Artificial Neural Network
Water supply
Water distribution
Neural network
Real time
Algorithm
Forecasting
Adaptation
ISSN:1364-8152
Online Access:Get full text
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Summary:This paper presents a novel methodology to perform adaptive Water Demand Forecasting (WDF) for up to 24 h ahead with the aim to support near real-time operational management of smart Water Distribution Systems (WDSs). The novel WDF methodology is exclusively based on the analysis of water demand time series (i.e., demand signals) and makes use of Evolutionary Artificial Neural Networks (EANNs). It is implemented in a fully automated, data-driven and self-learning Demand Forecasting System (DFS) that is readily transferable to practice. The main characteristics of the DFS are: (a) continuous adaptability to ever changing water demand patterns and (b) generic and seamless applicability to different demand signals. The DFS enables applying two alternative WDF approaches. In the first approach, multiple EANN models are used in parallel to separately forecast demands for different hours of the day. In the second approach, a single EANN model with a fixed forecast horizon (i.e., 1 h) is used in a recursive fashion to forecast demands. Both approaches have been tested and verified on a real-life WDS in the United Kingdom (UK). The results obtained illustrate that, regardless of the WDF approach used, the novel methodology allows accurate forecasts to be generated thereby demonstrating the potential to yield substantial improvements to the state-of-the-art in near real-time WDS management. The results obtained also demonstrate that the multiple-EANN-models approach slightly outperforms the single-EANN-model approach in terms of WDF accuracy. The single-EANN-model approach, however, still enables achieving good WDF performance and may be a preferred option in engineering practice as it is easier to setup/implement. •New short-term demand forecasting methodology able to adapt to changes in water consumption in near real-time.•Smart forecasting methodology based on Artificial Neural Networks and Evolutionary Algorithms.•Accurate water demand prediction with typical mean absolute percentage error around 5%.•Tested, validated and demonstrated on a real-life case study.•Encapsulated in a piece of software available for use in engineering practice.
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ISSN:1364-8152
DOI:10.1016/j.envsoft.2014.06.016