Urban water systems managementA data analytics approach

Urban w

ater systems m

anagement: a data analytics approach

Thomas Beach, Shaun H

owell, Yacine Rezgui9 781848 064751

ISBN 978-1-84806-475-1

Thomas Beach, Shaun Howell and Yacine Rezgui

Urban water systems managementUrban water systems are among the most critical of a nation’s infrastructure and are often complex, interdependent, spread out physically over a wide geographical area, and subject to various constraints such as a reliance on ageing assets.

The water industry has begun a transformation through the use of smart systems, including increased adoption of smart components such as sensors, analytics software, and decision support tools. However, current smart systems technologies are severely lacking in integration between smart components.

This publication focuses on how to integrate these smart components elements by using an approach driven by semantics to form a reference architecture that is driven by a semantic description of the smart water domain. Exemplar use cases of the reference architecture are presented along with a methodology of how a concrete implementation can be deployed on urban water systems.

About the authorsDr Thomas Beach is a lecturer in construction informatics who holds a PhD in computer science. His research focuses on innovation in the built environment field underpinned by state-of-the-art computing technologies. Dr Beach’s research interests include: specification and implementation of building/district/city data storage, the Internet of Things(IoT) and its application to the monitoring and control of the built environment, data analytics including machine learning and artificial intelligence, application of cloud/distributed computing to data storage and processing for built environment applications, and the semantics of data within the built environment.

Dr Shaun Howell is a senior machine learning researcher at Vivacity Labs, researching AI for the smart city mobility industry. He completed a PhD at the BRE Trust Centre of Excellence in Sustainable Engineering at Cardiff University, by researching semantic modelling and AI in the built environment, focusing on energy and water systems. Dr Howell has led and assisted across national and international projects, producing more than 30 publications. His research topics are applied deep neural networks, multi-agent systems, and semantic web technologies.

Professor Yacine Rezgui is a BRE (Building Research Establishment) Chair in Building Systems and Informatics, and Director of the BRE Trust Centre of Excellence in Sustainable Engineering at Cardiff University. He has over the last 20 years completed, in the capacity of principal investigator and co-investigator, over 40 research and development projects at a national and European level. He has over 200 refereed publications which have appeared in international journals such as IEEE Transactions on Systems, Man, and Cybernetics, Expert Systems with Applications, IEEE Transactions on Service Computing, and IEEE Transactions on Automation Science and Engineering.

Urban water systems managementA data analytics approach

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EP105.indd 1-3 15/03/2018 16:35:15

Thomas Beach, Shaun Howell and Yacine Rezgui

Urban water systems managementA data analytics approach

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Urban water systems management: A data analytics approach © 2018 T Beach, S Howell and Y Rezgui. All rights reserved

DisclaimerIHS Global Ltd is a private limited company registered in England and Wales (no. 00788737). Registered office: The Capitol Building, Oldbury, Bracknell, Berkshire RG12 8FZ. www.ihsmarkit.com.

IHS Markit publications are available from www.brebookshop.com or IHS Markit, Tel: +44 (0) 1344 328038, Fax: +44 (0) 1344 328005, Email: brepress@ihs.com.© 2018 T Beach, S Howell and Y Rezgui. All rights reserved. No part of this publication may be reproduced or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, or be stored in any retrieval system of any nature, without prior written permission of IHS Markit. Requests to copy any part of this publication should be made to: The Publisher, IHS Markit, Verulam Point, Station Way, St Albans, Herts AL1 5HE. Tel: +44 (0) 1727 733813, Email: brepress@ihs.com.

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Printed using FSC or PEFC material from sustainable forests.© 2018 T Beach, S Howell and Y Rezgui. All rights reserved.

Acknowledgements

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Elements of the work reported in this publication were supported by the EU Seventh Framework Program (FP7) under grant no. 619795.

ii | Acknowledgements

© 2018 T Beach, S Howell and Y Rezgui. All rights reserved Urban water systems management: A data analytics approach

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Contents

Glossary ivExecutive summary vii

1 Introduction 11.1 Motivation for improving data management in urban water systems 21.2 Summary 3

2 Background to urban water systems and semantics 42.1 Water abstraction 42.2 Water treatment 52.3 Potable water distribution 62.4 Water usage 72.5 Waste water collection 72.6 Waste water treatment and discharge 82.7 An introduction to semantic modelling 9

3 Data management in urban water systems 123.1 Managing large scale data 123.2 Contextualisation and management of data using a semantic approach 133.3 A reference architecture for data management 20

4 Using managed data to deliver analytics 4.1 An architecture to support water analytics 234.2 Eliciting new knowledge using analytics 234.3 Water network optimisation using deterministic models 254.4 Data driven modelling of water networks 274.5 Conclusion 27

5 A methodology for urban water analytic deployment 285.1 Capturing requirements and specifications 285.2 Deployment of generic software components 295.3 Deployment of water network-specific software components 305.4 Semantic modelling 305.5 Deploying analytic technologies 325.6 Developing analytics tools to allow for future modifications 32

6 Conclusions 33

References 34

Contents

© 2018 T Beach, S Howell and Y Rezgui. All rights reserved Urban water systems management: A data analytics approach

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analytics software, and decision support tools. However, current smart systems technologies are severely lacking in integration between their different elements. Furthermore, they lack the ability to contextualise the large amount of data collected from urban water systems in a way that promotes scalability, portability and future adaptability.

This publication focuses on how to rectify this problem by using an approach driven by semantics. Semantics is the study of meaning. In the context of smart technologies deployed in urban water systems, it means describing the physical, virtual, social, economic, and organisational elements and features within a smart water system, and the relationships between them, in a computer interpretable manner.

This publication proposes a viable solution to the problems of achieving efficient and scaleable use of large scale data in urban water systems, in the form of a reference architecture that uses a semantic description of the smart water domain. Exemplar use cases of the reference architecture are presented, along with a methodology of how a concrete implementation can be deployed on an urban water system.

Urban water systems are responsible for abstracting, treating and delivering clean water to consumers, and, subsequently, collecting, transporting, treating and releasing waste water safely back into the environment. These systems are among the most critical of a nation’s infrastructure and are often complex, interdependent, spead out physically over a wide geographical area, and subject to various constraints such as reliance on ageing assets. In many cases these systems are operating at capacity and frequently require expansion to respond to new developments and wider urban growth.

The adoption of new information communication technology (ICT) to enable the water network operators to intelligently monitor, analyse real-time information, and take appropriate actions, is seen as a key way of reducing the strain on urban water systems. This is achieved by increased efficiency instead of expansion. ICT acts as a key enabler to integrated water resources management – essentially the holistic management of water resources.

To this end, the water industry has begun a transformation through the use of smart systems. A big part of this is the increased adoption of sensors,

Executive summary

Executive summary

© 2018 T Beach, S Howell and Y Rezgui. All rights reserved Urban water systems management: A data analytics approach

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Urban infrastructure systems are complex, interdependent and subject to various constraints. They constantly require maintenance, retrofitting and renewal under tight financial constraints, while at the same time operate at capacity and require expansion to respond to new developments and wider urban growth. It is becoming essential to work towards a tight integration between building systems (in particular utility systems, including energy and water) with infrastructure systems at a district and wider city level. This will help alleviate the current endemic ‘infrastructure gap’ with a new value proposition centred on sustainability and resilience.

Infrastructures are interdependent within a complex urban fabric. They rely on various sources of financing and investment, including government, industry and specific local taxation. Infrastructure is also maintained and developed in a very fragmented landscape under the control of a wide range of regulatory bodies.

Urban water systems are responsible for abstracting, treating and delivering clean water to their consumers, and, subsequently, collecting, transporting, treating and releasing waste water. These systems are among the most critical and complex, spread over a wide geographical area using ageing infrastructure.

In this context, the demand on water resources in the urban environment requires more efficient water management to deal with urbanisation and population growth, more complex water facilities in new buildings, and the deterioration of existing water infrastructure. There is an urgent need to reduce the water abstracted for use in buildings, promote water savings, stop wastage, and promote the use of water-use performance metrics. The ability to intelligently monitor the water network in urban environments, and analyse real-time information using smart technologies, will provide optimised alternatives to take better actions to balance the conflict between water demand and provision[1].

In order to reduce water use in buildings, and by extension in urban areas, several options are available[1, 2], including:

• raising awareness to improve behaviours

• changing the pricing policies

• metering water to both detect leaks and raise consumer awareness

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Introduction

• limiting water use by regulatory requirements

• installing more efficient water-using products

• planning buildings so they are more water efficient

• using ‘alternative’ water sources, eg rainwater or greywater.

In fact, there is a tendency for water wastage simply due to the way water is allocated and valued. This is currently based upon historical rights, using ‘first-come-first-served’ principles. Water is also ‘locked up’ in abstraction licences because most abstractions only require a third to half of their entitlement. Water allocation does not currently take account of optimising resource availability for water consumption while protecting the environment, or has yet to practically address future demand through population growth and food security. Neither of these approaches has developed sustainable mechanisms to adapt to unpredictable weather patterns due to climate change.

The nexus between energy and water is almost inevitable, as the two are intimately linked. Various industries, including the steel industry, have open abstraction licences for water. Evidence suggests that the more water they abstract, the more energy they consume. The European Commission has defined a clear 2020 target to reduce CO2 emissions by 20%, and increase the share of renewable energies by 20%. These objectives have been translated into stringent regulations and policies at the European and national levels. For instance, the recast of the Energy Performance of Buildings Directive (2010/31/EU) imposes stringent requirements in terms of energy efficiency for new and retrofitted buildings. At building scale, a great potential exists for energy recovery from domestic water and from bathroom greywater. However, to optimise the benefits of energy-saving methods the components must be maintained.

Environmental assessment methods such as the Code for Sustainable Homes and BREEAM in the UK, HQE in France, and the Sustainable Buildings Alliance (SBA) at the international level, factor in water management. The assessment methods aim to reduce the consumption of potable water for sanitary use in new buildings through the use of water-efficient components and water recycling systems. Credits can be gained that can contribute to higher environmental grading. However, unlike for energy, there is no requirement to meet a given water consumption target or water performance specification.

1 Introduction