Energy Cloud: Real-Time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites

Sequeira, Hugo; Carreira, Paulo; Goldschmidt, Thomas; Vorst, Philipp

doi:10.1109/ucc.2014.79

Cited by 42 publications

(41 citation statements)

References 14 publications

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“…Figure 3 provides an example of such an energy visualization dashboard [29], which uses gauges not only for showing the current total power consumption (at the bottom center) but also the amount of power currently being generated from different power sources (on either sides). Similar examples of the use of energy dashboards, with gauges and other visualizations, have been provided by Sequeira et al [30]. The assumption regarding the usefulness of gauges on a dashboard is generally valid while driving a car and therefore, an indicator showing whether the current style of driving is, for instance, economical or not is likely to have a positive impact on drivers' behavior.…”

Section: Energy Gaugesmentioning

confidence: 77%

Promoting Sustainable Energy Consumption Behavior through Interactive Data Visualizations

Rist

Masoodian

2019

MTI

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With an increasing worldwide need for energy and the ever-decreasing availability of energy resources, a wide range of interactive visualizations are being developed to allow people to use energy more efficiently by monitoring their consumption patterns and changing their energy usage behavior. For these visualizations to achieve their aim, they must not only target people’s energy saving objectives but also support the necessary factors that help people to change their underlying energy consumption behavior. In this paper, we survey several categories of existing interactive energy visualizations and through a number of selected examples in each case, identify possible potentials for supporting any user behavior changes. For this survey, we have used the behavior change model originally proposed by B. J. Fogg, which defines three factors of motivation, trigger and ability. Our survey has shown that most existing interactive visualizations target the motivation factor, with some supporting trigger or ability and only a few dealing with all the three factors of the behavior change model.

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Section: Energy Gaugesmentioning

confidence: 77%

Promoting Sustainable Energy Consumption Behavior through Interactive Data Visualizations

Rist

Masoodian

2019

MTI

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“…In the fourth industrial revolution (Industry 4.0), products and production systems leverage IoT and Big Data to build ad-hoc networks for self-control and self-optimization (O'Donovan et al 2015). Big Data poses a host of challenges to Industry 4.0, including the following: (i) seamless integration of energy and production; (ii) centralization of data correlations from all production levels; (iii) optimization of performance of scheduling algorithms (Sequeira et al 2014;Gui et al 2016); (iv) storage of Big Data in a semi-structured data model to enable real-time queries and random access without time-consuming operations and data joins (Kagermann et al 2013) and (v) realization of on-the-fly analysis to help organizations react quickly to unanticipated events and detect hidden patterns that compromise production efficiency (Sequeira et al 2014). Cloud computing could be leveraged to tackle these challenges in Industry 4.0 for networking, data integration, data analytics (Gölzer, Cato, and Amberg 2015) and intelligence for Cyber-Physical Systems and resiliency and self-adaptation (Krogh 2008).…”

Section: Industrymentioning

confidence: 99%

Big Data and cloud computing: innovation opportunities and challenges

Yang

Huang

et al. 2016

International Journal of Digital Earth

645

289

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Big Data has emerged in the past few years as a new paradigm providing abundant data and opportunities to improve and/or enable research and decision-support applications with unprecedented value for digital earth applications including business, sciences and engineering. At the same time, Big Data presents challenges for digital earth to store, transport, process, mine and serve the data. Cloud computing provides fundamental support to address the challenges with shared computing resources including computing, storage, networking and analytical software; the application of these resources has fostered impressive Big Data advancements. This paper surveys the two frontiers-Big Data and cloud computingand reviews the advantages and consequences of utilizing cloud computing to tackling Big Data in the digital earth and relevant science domains. From the aspects of a general introduction, sources, challenges, technology status and research opportunities, the following observations are offered: (i) cloud computing and Big Data enable science discoveries and application developments; (ii) cloud computing provides major solutions for Big Data; (iii) Big Data, spatiotemporal thinking and various application domains drive the advancement of cloud computing and relevant technologies with new requirements; (iv) intrinsic spatiotemporal principles of Big Data and geospatial sciences provide the source for finding technical and theoretical solutions to optimize cloud computing and processing Big Data; (v) open availability of Big Data and processing capability pose social challenges of geospatial significance and (vi) a weave of innovations is transforming Big Data into geospatial research, engineering and business values. This review introduces future innovations and a research agenda for cloud computing supporting the transformation of the volume, velocity, variety and veracity into values of Big Data for local to global digital earth science and applications.

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“…As a serving layer, KairosDB, timeseries database built on top of Apache Cassandra, could handle the workload of a large city with around six million smart meters. During this experiment, KairosDB was installed in a cluster of 24 nodes [19].…”

Section: Fig 2 Kappa Architecturementioning

confidence: 99%

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

Lipcak¹,

Macák²,

Rossi³

2019

Annals of Computer Science and Information Systems

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Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).

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Energy Cloud: Real-Time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites

Cited by 42 publications

References 14 publications

Promoting Sustainable Energy Consumption Behavior through Interactive Data Visualizations

Promoting Sustainable Energy Consumption Behavior through Interactive Data Visualizations

Big Data and cloud computing: innovation opportunities and challenges

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

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