Real-Time Business Intelligence in the MIRABEL Smart Grid System

Fischer, Ulrike; Kaulakienė, Dalia; Khalefa, Mohamed E.; Lehner, Wolfgang; Pedersen, Torben Bach; Šikšnys, Laurynas; Thomsen, Christian

doi:10.1007/978-3-642-39872-8_1

Cited by 5 publications

(8 citation statements)

References 17 publications

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“…TimeTravel [68], [69] Forecasting, AQP F 2 DB [70], [71] Forecasting, AQP Unamed [72], [73], [74] Interpolation, AQP In addition to the classification criteria shown in Table 1, the TSMSs are summarized with regard to their supported query functionality in Table 2. To describe the functionality of the surveyed TSMSs with a uniform set of well-known terms, we elected to primarily express it using SQL.…”

Section: Rdbms Extensionsmentioning

confidence: 99%

“…In terms of stream processing, TimeTravel provides no extensions to PostgreSQL other than the module for maintaining the model index automatically. The methods created as part of TimeTravel have been incorporated into a Electricity Data Management System (EDMS) developed as part of the MIRABEL smart grid project [82] by Fisher et al [69]. To facilitate both exact and approximated analysis of time series in the EDMS, a real-time data warehouse is implemented as part of the system.…”

Section: Systemsmentioning

confidence: 99%

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Time Series Management Systems: A Survey

Jensen

Pedersen

Thomsen

2017

IEEE Trans. Knowl. Data Eng.

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137

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The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD

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Section: Rdbms Extensionsmentioning

confidence: 99%

Section: Systemsmentioning

confidence: 99%

Time Series Management Systems: A Survey

Jensen

Pedersen

Thomsen

2017

IEEE Trans. Knowl. Data Eng.

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137

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“…According to the MIRABEL project, an energy enterprise continuously perform complex energy planning and control activities [1,5,12] to achieve a multi-objective goal, which is to (1) satisfy all flex-offers, (2) utilize as much RES energy as possible, (3) balance the grid, and (4) maximize profit.…”

Section: Energy Planning and Control In Mirabel Enterprisementioning

confidence: 99%

“…In addition to the conventional SCADA (supervisory control and data acquisition) and ERP (Enterprise resource planning) systems, it employs a comprehensive energy data management system (EDMS) [5,12] with the inherent support for flex-offers and offering flex-offer storage [23] and advanced analytic functionality such as aggregation [28], scheduling [27], and forecasting [11].…”

Section: Energy Planning and Control In Mirabel Enterprisementioning

confidence: 99%

Visualizing complex energy planning objects with inherent flexibilities

Šikšnys

Kaulakienė

2013

Proceedings of the Joint EDBT/ICDT 2013 Workshops

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Energy distribution enterprises will face the challenge to plan and balance consumption and production in the electricity grid with the substantial amount of renewable energy sources (RES) [4]. To effectively plan and balance energy, a potential future energy enterprise will have to exploit flexibilities of individual energy consumers (and producers) to be able to delay certain loads of energy for some time interval. Such type of flexibility can be represented with a flex-offer from the MIRABEL project [1]. In this paper, we discuss energy planning and controlling activities within such future energy enterprise and, to support those activities, we propose a comprehensive visual analysis framework that provides detailed and generalized views over flex-offers, and offers a convenient OLAP-inspired approach to navigate and explore such data. Then, as the first step developing such framework, we focus on the detail view and present the implementation of the histogram-based approach to visualize a large number of individual flex-offers. Finally, we review the related work in the areas of the multidimensional, spatial, temporal, spatio-temporal, and energy data visualization, and present the future work.

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“…The figure also shows the flexibilities in the energy profile, specifically, at each (15 min) time interval it states the minimum and maximum required energy (solid and dotted area respectively). The MIRABEL project prototypes the data management infrastructure which enables near real-time flex-offer collection [3], aggregation [4], and scheduling [5], in addition to the reliable and near real-time forecasting of energy production and consumption [6]. To sum up, the flex-offer concept is the basis of the project.…”

Section: Introductionmentioning

confidence: 99%

Towards the automated extraction of flexibilities from electricity time series

Kaulakienė

Šikšnys

Pitarch

2013

Proceedings of the Joint EDBT/ICDT 2013 Workshops

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Several recent and ongoing smart grid projects aim at incorporating more renewable energy sources (RES) into the energy production. Among them, the European MIRABEL project tackles this problem by managing flexibilities on energy demand and supply. Typically, this project assumes that some parts of the energy demand can be shifted when the RES production is sufficient, e.g., the washing machine can be turned on when the wind blows. To express these flexibilities, the project introduces the core-concept of flexoffer. Unfortunately, flex-offer data from the consumers is not yet available. Consequently, in order to test and evaluate the MIRABEL prototype, the flex-offers are extracted from the real world electricity consumption time series. In this work, we investigate, discuss, and experiment several ways to automatically capture flexibility within the electricity time series. Particularly, we show that incorporating domain knowledge, for instance, appliance information or appliance usage frequencies, can improve a lot the outcome of the flex-offer generation and, thus, the MIRABEL project global evaluation.

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Real-Time Business Intelligence in the MIRABEL Smart Grid System

Cited by 5 publications

References 17 publications

Time Series Management Systems: A Survey

Time Series Management Systems: A Survey

Visualizing complex energy planning objects with inherent flexibilities

Towards the automated extraction of flexibilities from electricity time series

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