Integrated intelligent water-energy metering systems and informatics: Visioning a digital multi-utility service provider

Stewart, Rodney Anthony; Nguyen, Khoi; Beal, Cara; Zhang, Hong; Sahin, Oz; Bertone, Edoardo; Vieira, Abel Silva; Castelletti, Andrea; Cominola, Andrea; Giuliani, Matteo; Giurco, Damien; Blumenstein, Michael; Turner, Andrea; Liu, Ariane; Kenway, Steven; Savić, Dragan; Makropoulos, Christos; Kossieris, Panagiotis

doi:10.1016/j.envsoft.2018.03.006

Cited by 83 publications

(57 citation statements)

References 130 publications

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“…This information sharing potentially exposes customers to privacy invasions with the main concern being the limited control over personal data by an individual, which can result in a range of negative or unintended consequences. Legal considerations relating to privacy and data protection with respect to services or applications created using customer water usage data (particularly valuable when combined with personal data), has been given insufficient attention in the literature [120]. It is, therefore, positive that the new EU General Data Protection Regulation (GDPR) [121] provides a framework for data protection and privacy for citizens.…”

Section: Some Words Of Cautionmentioning

confidence: 99%

Urban Hydroinformatics: Past, Present and Future

Makropoulos

Savić

2019

Water

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Hydroinformatics, as an interdisciplinary domain that blurs boundaries between water science, data science and computer science, is constantly evolving and reinventing itself. At the heart of this evolution, lies a continuous process of critical (self) appraisal of the discipline’s past, present and potential for further evolution, that creates a positive feedback loop between legacy, reality and aspirations. The power of this process is attested by the successful story of hydroinformatics thus far, which has arguably been able to mobilize wide ranging research and development and get the water sector more in tune with the digital revolution of the past 30 years. In this context, this paper attempts to trace the evolution of the discipline, from its computational hydraulics origins to its present focus on the complete socio-technical system, by providing at the same time, a functional framework to improve the understanding and highlight the links between different strands of the state-of-art hydroinformatic research and innovation. Building on this state-of-art landscape, the paper then attempts to provide an overview of key developments that are coming up, on the discipline’s horizon, focusing on developments relevant to urban water management, while at the same time, highlighting important legal, ethical and technical challenges that need to be addressed to ensure that the brightest aspects of this potential future are realized. Despite obvious limitations imposed by a single paper’s ability to report on such a diverse and dynamic field, it is hoped that this work contributes to a better understanding of both the current state of hydroinformatics and to a shared vision on the most exciting prospects for the future evolution of the discipline and the water sector it serves.

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Section: Some Words Of Cautionmentioning

confidence: 99%

Urban Hydroinformatics: Past, Present and Future

Makropoulos

Savić

2019

Water

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“…by burglars interested in home occupancy or by snooping family members [10]. Furthermore, water data can be combined with electricity data to paint a more detailed picture of behavior, by which individuals could be tracked [11]. In this setting, the challenge will be to strike a balance between data use and data protection.…”

Section: Water Tells More Than You Knowmentioning

confidence: 99%

Smart urban water systems: what could possibly go wrong?

Vitry

Schneider

Wani

et al. 2019

Environ. Res. Lett.

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Urban water systems are being redefined for a digital age, promising substantial advantages for service users and providers, and for society as a whole. However, beside the much-discussed benefits of smart urban water systems in future smart cities, the transition will also bring unique challenges, particularly with respect to privacy and cybersecurity. A preemptive delineation of these risks and providing appropriate recommendations will help to guide digital transformations of urban water towards sustainable solutions. Faced with emerging risks of digitalization, urban water management needs to look beyond technology while recognizing the central and multifaceted role of research.In smart city initiatives, urban water systems remain largely out of the digitalization spotlight compared to other types of infrastructure. Oftentimes, the management and planning of urban water systems still follow traditional, steel and concrete-based approaches that treat used water as waste and not as a resource [1]. The need for innovation, however, is significant: the infrastructure, operation, and maintenance cost of urban water systems around the world already ranges in the hundred billion dollars annually and water-related stresses are expected to continue rising. Digitalization could not only increase the flexibility and effectiveness of existing urban water systems, but could also allow for the provisions of new services to society. The envisioned transformation, as outlined for example in the literature [2][3][4], entails novel data collection and transmission techniques, analytical methods, models, and automation. For example, smart water meter data can be combined with noise loggers to detect and roughly locate leaks in water mains. Together with automated control infrastructure, pressure can be regulated to reduce water losses [4]. By this and numerous other smart solutions, not only water distribution but also sewers and wastewater treatment will be transformed. In particular, digitalization facilitates large-scale implementation of disruptive technologies like decentralized wastewater treatment [5] and direct potable reuse, which will help cities to cope with climate change and urbanization.However, we argue that as the benefits of smart urban water systems are increasingly explored and showcased in literature, it is time to also give attention to potential risks that might emerge. Recurring data breaches and the growing use of cyber-attacks for geopolitical ends are a reminder that the digitalization of society indeed carries risks. Given the critical role of water services in society and within the water-energyfood nexus, these risks must be identified and managed pro-actively. In the following, we raise awareness and suggest ways forward in approaching the unique privacy and security issues that accompany the digitalization of urban water systems. Finally, we highlight the central role of researchers in assessing and mitigating the potential risks of the smart urban water solutions they propose. Water tells more th...

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“…This approach utilises intelligent machine learning algorithms for the end use disaggregation task. Some of notable studies includes (Pastor-Jabaloyes et al 2018), (Cardell-Oliver et al 2016), BuntBrain-ForEndUses® by (Arregui 2015), REU2016 by Webber 2016, 2018), SmartH20 by (Cominola et al 2015 and Autoflow by (Nguyen et al 2015;Stewart et al 2018). The latter mentioned Autoflow software uses a combination of different pattern recognition and data mining techniques including HMM, ANN, DTW, histogram analysis and time-of-day probability function to automate the end use analysis process.…”

Section: Existing Autonomous Water End Use Classification Applicationsmentioning

confidence: 99%

An adaptive model for the autonomous monitoring and management of water end use

et al. 2018

Self Cite

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Most pattern classification systems are usually developed based on the training of historical data, and as a result, the performance of these models relies heavily on the amount of collected information. However, in many cases, such data collection process is relatively costly, which eventually limits the efficiency as well as the widespread implementation of the final developed model. In this context, the paper focuses on presenting an advanced universal water management system, which could interface with both water consumers and utilities via smart phone and web application. Originally, Autoflow©, a prototype tool that is used to disaggregate total water consumption into each end-use category was developed, which achieved an accuracy ranging from 74 to 94%. However, a drawback of this model was that it was trained with data collected from only Australia; therefore, accuracy reductions would likely be observed when this system is implemented in different countries having very different water using appliances and behaviour patterns. To avoid the costly data collection process for model calibration when operating in new regions, this research study introduces an enhanced model, namely Autoflow U (i.e. U stands for Universal). This new tool can be applied in residential properties globally to autonomously disaggregate water consumption into the seven main water end-use categories, namely: shower, toilet, tap, clothes washer, dishwasher, evaporative air cooler and irrigation, without the need for collecting new regional end-use data for model calibration. In order to develop this new tool, Decision Trees, Dynamic Time Warping (DTW), Self Organising Map (SOM) and Hidden Markov Model (HMM) techniques were utilised. The test results obtained from 230 properties in both Australia and the US showed that the Autoflow U achieved 72-93% accuracy.

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Integrated intelligent water-energy metering systems and informatics: Visioning a digital multi-utility service provider

Cited by 83 publications

References 130 publications

Urban Hydroinformatics: Past, Present and Future

Urban Hydroinformatics: Past, Present and Future

Smart urban water systems: what could possibly go wrong?

An adaptive model for the autonomous monitoring and management of water end use

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