Smart meter data: Balancing consumer privacy concerns with legitimate applications

McKenna, Eoghan; Richardson, Ian; Thomson, Murray

doi:10.1016/j.enpol.2011.11.049

Cited by 312 publications

(148 citation statements)

References 17 publications

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“…The implementation of smart meters and smart sensors being one area generating large amounts of near-real-time information, permitting water companies to deliver better services and improved infrastructure, drawing upon Big Data techniques [36][37][38][39][40][41]. However, the application of these technologies across other business activities, such as the better characterization of water effluents, or the better understanding of the water quality, seem as yet relatively unexplored.…”

Section: Current Use In the Water Sectormentioning

confidence: 99%

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

2017

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Abstract:The water utility sector is subject to stringent legislation, seeking to address both the evolution of practices within the chemical/pharmaceutical industry, and the safeguarding of environmental protection, and which is informed by stakeholder views. Growing public environmental awareness is balanced by fair apportionment of liability within-sector. This highly complex and dynamic context poses challenges for water utilities seeking to manage the diverse chemicals arising from disparate sources reaching Wastewater Treatment Plants, including residential, commercial, and industrial points of origin, and diffuse sources including agricultural and hard surface water run-off. Effluents contain broad ranges of organic and inorganic compounds, herbicides, pesticides, phosphorus, pharmaceuticals, and chemicals of emerging concern. These potential pollutants can be in dissolved form, or arise in association with organic matter, the associated risks posing significant environmental challenges. This paper examines how the adoption of new Big Data tools and computational technologies can offer great advantage to the water utility sector in addressing this challenge. Big Data approaches facilitate improved understanding and insight of these challenges, by industry, regulator, and public alike. We discuss how Big Data approaches can be used to improve the outputs of tools currently in use by the water industry, such as SAGIS (Source Apportionment GIS system), helping to reveal new relationships between chemicals, the environment, and human health, and in turn provide better understanding of contaminants in wastewater (origin, pathways, and persistence). We highlight how the sector can draw upon Big Data tools to add value to legacy datasets, such as the Chemicals Investigation Programme in the UK, combined with contemporary data sources, extending the lifespan of data, focusing monitoring strategies, and helping users adapt and plan more efficiently. Despite the relative maturity of the Big Data technology and adoption in many wider sectors, uptake within the water utility sector remains limited to date. By contrast with the extensive range of applications of Big Data in in other sectors, highlight is drawn to how improvements are required to achieve the full potential of this technology in the water utility industry.

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Section: Current Use In the Water Sectormentioning

confidence: 99%

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

2017

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“…It was proven that sampling times of 1 minute or shorter are suitable to capture the fine details of household load patterns [33]. In contrast, most of the currently installed smart meters log the consumption data hourly, half-hourly or at 15 minutes intervals [19]. Furthermore, a sampling time of 15 minutes for the aggregate residential consumption data is adequate for its use in network management based on economic dispatch as energy markets commonly operate with intervals of 30 minutes or longer.…”

Section: Energy Demand Time Seriesmentioning

confidence: 99%

Application-oriented modelling of domestic energy demand

Gruber

Jahromizadeh

Prodanović

et al. 2014

International Journal of Electrical Power & Energy Systems

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractDetailed residential energy consumption data can be used to offer advanced services and provide new business opportunities to all participants in the energy supply chain, including utilities, distributors and customers. The increasing interest in the residential consumption data is behind the roll-out of smart meters in large areas and led to intensified research efforts in new data acquisition technologies for the energy sector. This paper introduces a novel model for generation of residential energy consumption profiles based on the energy demand contribution of each household appliance and calculated by using a probabilistic approach. The model takes into consideration a wide range of household appliances and its modular structure provides a high degree of flexibility. Residential consumption data generated by the proposed model are suitable for development of new services and applications such as residential real-time pricing schemes or tools for energy demand prediction. To demonstrate the main features of the model, an individual household consumption was created and the effects of a possible change in the user behaviour and the appliance configuration presented. In order to show the flexibility offered in creation of the aggregated demand, the detailed simulation results of an energy demand management algorithm applied to an aggregated user group are used.

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“…In addition to the substantial investments necessary for the consumers' premises, their consent is also required. Concerns relating to data privacy and security could be veritable roadblocks here (McKenna et al, 2012).…”

Section: Managerial and Policy Implicationsmentioning

confidence: 99%

Value and granularity of ICT and smart meter data in demand response systems

2016

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The large-scale integration of intermittent resources of power generation leads to unprecedented fluctuations on the supply side. An electricity retailer can tackle these challenges by pursuing strategies of flexible load shifting -socalled demand response mechanisms. This work addresses the associated tradeoff between ICT deployment and economic benefits. The ICT design of a demand response system serves as the basis of a cost-value model, which incorporates all relevant cost components and compares them to the expected savings of an electricity retailer. Our analysis is based on a typical German electricity retailer to determine the optimal read-out frequency of smart meters. For our set of parameters, a positive information value of smart meter read-outs is achieved within an interval of 21 to 57 minutes regarding variable costs.Electricity retailers can achieve a profitable setting by restricting smart meter roll-out to large customers.

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Smart meter data: Balancing consumer privacy concerns with legitimate applications

Cited by 312 publications

References 17 publications

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

Leveraging Big Data Tools and Technologies: Addressing the Challenges of the Water Quality Sector

Application-oriented modelling of domestic energy demand

Value and granularity of ICT and smart meter data in demand response systems

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