Special Issue "Role of Intelligent Sensor Networks and Big Data Informatics for Enhanced Management of Urban Water and Energy Resources"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Urban Water Management".

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editor

Prof. Dr. Rodney Stewart
E-Mail Website
Guest Editor
School of Engineering and Built Environment, Griffith University, Parklands Drive, Southport, QLD 4222, Australia
Interests: digital engineering; building information modelling; digital information asset management; digital utility transformation; smart or intelligent water and energy metering; intelligent sensor networks; remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The traditional delivery of urban water and energy resources has been a conservative process whereby utilities offered a relatively unsophisticated service to their customers. As expectations to provide a leaner, greener, and customer-focused utility rise, it has become clear that conventional means of water and energy provision are no longer adequate in the digital information age. As the momentum gathers, there is growing pressure on the utility sector to transition to the digital age.

An intelligent water or energy grid refers to the integration and remote communication of information via supporting technologies such as sensors, meters, and computerized controls that continuously and remotely monitor the water, electricity, or gas distribution system. The introduction and advancement of these advanced enabling technologies has allowed an expanded capacity to monitor many different parameters. For water distribution, this includes pressure, quality, flow rates, temperature, and leaks, to name a few. In energy distribution systems, losses and theft, peak load shifting, resource storage, and time of day demand are all key features of a smart energy grid.

Developing technologies and the accompanying big data informatics, once fully understood and exploited, are the truly “smart” components of a digital water, electricity, or gas grid, and these informatics can be used for a range of applications. Informatics applying statistical, mathematical, machine learning, and rule-based approaches can be used to provide important information on-demand from the available data provided at intervals of seconds, minutes, or hours. Such information is powerful for government, utility and customer planning and decision making.

Moreover, the acknowledgement of water–energy links is emerging as a key pathway for the integration of water and energy multi-utility services provision. Such integrated contemporaneous multi-utility information can be used by utilities and customers to explore a range of efficient technologies and demand management strategies that can be used to reduce household water and energy consumption.

The Guest Editor is seeking papers that demonstrate how intelligent sensor technologies and big data analytics can be used to enhance the current paradigm of management of urban water and energy grids. Works should demonstrate novel concepts and applications within one or more aspects of the entire network from generation/supply through to distribution and the end-user/customer. Operational, customer, economic, finance, social, engineering, science, forecasting, and asset management dimensions are encouraged. The use of advanced data mining and pattern recognition techniques for providing more efficient and effective ways to manage urban water and energy resources are solicited. Papers that explore and exploit the interaction between water–energy data for a range of applications are particularly encouraged. It is hoped that Special Issue papers will aid the water and energy utility sectors in seamlessly transitioning to the digital multi-utility era.

Prof. Dr. Rodney Stewart
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart metering
  • energy and water demand management
  • intelligent water networks
  • smart grids
  • intelligent energy networks
  • digital utility transformation

Published Papers (6 papers)

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Research

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Article
An Advanced Sensor Placement Strategy for Small Leaks Quantification Using Lean Graphs
Water 2020, 12(12), 3439; https://0-doi-org.brum.beds.ac.uk/10.3390/w12123439 - 08 Dec 2020
Viewed by 683
Abstract
Small leaks in water distribution networks have been a major problem both economically and environmentally, as they go undetected for years. We model the signature of small leaks as a unique Directed Acyclic Graph, called the Lean Graph, to find the best places [...] Read more.
Small leaks in water distribution networks have been a major problem both economically and environmentally, as they go undetected for years. We model the signature of small leaks as a unique Directed Acyclic Graph, called the Lean Graph, to find the best places for k sensors for detecting and locating small leaks. We use the sensors to develop dictionaries that map each leak signature to its location. We quantify leaks by matching out-of-normal flows detected by sensors against records in the selected dictionaries. The most similar records of the dictionaries are used to quantify the leaks. Finally, we investigate how much our approach can tolerate corrupted data due to sensor failures by introducing a subspace voting based quantification method. We tested our method on water distribution networks of literature and simulate small leaks ranging from [0.1, 1.0] liter per second. Our experimental results prove that our sensor placement strategy can effectively place k sensors to quantify single and multiple small leaks and can tolerate corrupted data up to some range while maintaining the performance of leak quantification. These outcomes indicate that our approach could be applied in real water distribution networks to minimize the loss caused by small leaks. Full article
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Article
Expert Opinion Valuation Method to Quantify Digital Water Metering Benefits
Water 2020, 12(5), 1436; https://0-doi-org.brum.beds.ac.uk/10.3390/w12051436 - 18 May 2020
Cited by 5 | Viewed by 1244
Abstract
Business cases promoting the introduction of digital water metering (DWM) have, to date, focused on a limited number of benefits, especially water savings, metering costs, occupational health and safety (OHS), and deferral of capital works. An earlier study by the authors catalogued 75 [...] Read more.
Business cases promoting the introduction of digital water metering (DWM) have, to date, focused on a limited number of benefits, especially water savings, metering costs, occupational health and safety (OHS), and deferral of capital works. An earlier study by the authors catalogued 75 possible benefits and developed a taxonomy based on a literature review, interviews and water industry reports. The objective of the present study was to elicit the opinions of Australian water industry experts on the benefits, then use the opinions to form probability distributions which, in future work, could be used to model the value of DWM benefits. The study findings have implications for researchers and practitioners seeking to accurately and stochastically model the benefits of DWM transformation programmes. Thematic analyses on the open ended responses scaled likelihood and estimated value of benefits into comparable units. We found 82% support for the benefits of DWM with only 6% disagreement and 12% non-commital; the savings value of cost of water benefits were predominately expected to range between 5% and 10% and much higher in some individual situations, while charges/operational costs benefits were predominately expected to range between 45% and 100%; and, moreover, we indicated how a risk-based range of project benefit could potentially be calculated. Opportunities for further investigations were identified. Full article
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Article
The Use of Digital Terrain Models to Estimate the Pace of Filling the Pit of a Central European Granite Quarry with Water
Water 2019, 11(11), 2298; https://0-doi-org.brum.beds.ac.uk/10.3390/w11112298 - 02 Nov 2019
Cited by 3 | Viewed by 1080
Abstract
This paper presents the results of an analysis of the pace of filling one of the deepest European granite quarries with water. A DTM (digital terrain model) based on data from LiDAR ALS (light detection and ranging airborne laser scanning) was used to [...] Read more.
This paper presents the results of an analysis of the pace of filling one of the deepest European granite quarries with water. A DTM (digital terrain model) based on data from LiDAR ALS (light detection and ranging airborne laser scanning) was used to create a model of the pit of the Strzelin I granite quarry and to determine the reach and surface area of the direct catchment of the excavation pit. The increase in the volume of water in the excavation pit was determined. Analogue maps and DTM were used to calculate the maximum depth of the pit (113.3 m), its surface area (9.71 ha), and its capacity (5.1 million m3). The volume of water collected in the excavation pit during the years 2011–2018 was determined based on the analogue base map and the DTM. The result was 0.335 million m3. Based on the data made available by the mining company, the correlation of the DTM with the orthophotomap of the mining area and additional field measurements, the ordinates of the water level in the years 2011–2018 were determined. Initially, the water surface level in the quarry was located on the ordinate of 66.6 m a.s.l. (July 20, 2011). After the pumping of water was discontinued, the level rose to 96.1 m a.s.l. (January 28, 2018). The increase in the water volume in the quarry pit during specific periods was determined (actual retention increase). The obtained data on the volume of the retained water referred to the period during which it accumulated in the quarry. On average, the net increase in water retention in the excavation pit was 138.537 m3∙d−1, and the calculated net supply from the direct catchment (16.04 ha) was 101.758 m3∙d−1. The use of DTM and measurements of the water level in the excavation pit seem to be an efficient means of estimating the pace of spontaneous filling of the quarry with water supplied from the direct physiographic catchment. Full article
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Review

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Review
Monitoring Approaches for Faecal Indicator Bacteria in Water: Visioning a Remote Real-Time Sensor for E. coli and Enterococci
Water 2020, 12(9), 2591; https://0-doi-org.brum.beds.ac.uk/10.3390/w12092591 - 16 Sep 2020
Cited by 1 | Viewed by 1085
Abstract
A comprehensive review was conducted to assess the current state of monitoring approaches for primary faecal indicator bacteria (FIB) E. coli and enterococci. Approaches were identified and examined in relation to their accuracy, ability to provide continuous data and instantaneous detection results, cost, [...] Read more.
A comprehensive review was conducted to assess the current state of monitoring approaches for primary faecal indicator bacteria (FIB) E. coli and enterococci. Approaches were identified and examined in relation to their accuracy, ability to provide continuous data and instantaneous detection results, cost, environmental awareness regarding necessary reagent release or other pollution sources, in situ monitoring capability, and portability. Findings showed that several methods are precise and sophisticated but cannot be performed in real-time or remotely. This is mainly due to their laboratory testing requirements, such as lengthy sample preparations, the requirement for expensive reagents, and fluorescent tags. This study determined that portable fluorescence sensing, combined with advanced modelling methods to compensate readings for environmental interferences and false positives, can lay the foundations for a hybrid FIB sensing approach, allowing remote field deployment of a fleet of networked FIB sensors that can collect high-frequency data in near real-time. Such sensors will support proactive responses to sudden harmful faecal contamination events. A method is proposed to enable the development of the visioned FIB monitoring tool. Full article
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Review
Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review
Water 2020, 12(1), 294; https://0-doi-org.brum.beds.ac.uk/10.3390/w12010294 - 19 Jan 2020
Cited by 14 | Viewed by 2600
Abstract
Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback [...] Read more.
Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advanced ML and DA techniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours. Full article
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Review
Revealing Unreported Benefits of Digital Water Metering: Literature Review and Expert Opinions
Water 2019, 11(4), 838; https://0-doi-org.brum.beds.ac.uk/10.3390/w11040838 - 20 Apr 2019
Cited by 18 | Viewed by 2483
Abstract
Digital water meters can take Australian water utilities into the world of internet of things (IoT) and big data analytics. The potential is there for them to build more efficient processes, to enable new products and services to be offered, to defer expensive [...] Read more.
Digital water meters can take Australian water utilities into the world of internet of things (IoT) and big data analytics. The potential is there for them to build more efficient processes, to enable new products and services to be offered, to defer expensive capital works, and for water conservation to be achieved. However, utilities are not mounting business cases with sufficient benefits to cover the project and operational costs. This study undertakes a literature review and interviews of industry experts in the search for unreported benefits that might be considered for inclusion in business cases. It identifies seventy-five possible benefits of which fifty-seven are classified as benefiting the water utility and forty are classified as benefiting customers (twenty-two benefit both). Many benefits may be difficult to monetize. Benefits to customers may have a small monetary benefit to the water utility but provide a significant benefit to customer satisfaction scores. However, for utilities to achieve these potential benefits, eight change enablers were identified as being required in their systems, processes, and resources. Of the seventy-five benefits, approximately half might be considered previously unreported. Finally, a taxonomy is presented into which the benefits are classified, and the enabling business changes for them to be realized are identified. Water utilities might consider the taxonomy, the benefits, and the changes required to enable the benefits when developing their business cases. Full article
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