Big Data Analytics for Dynamic Energy Management in Smart Grids

Diamantoulakis, Panagiotis D.; Kapinas, Vasileios M.; Karagiannidis, George K.

doi:10.1016/j.bdr.2015.03.003

Cited by 263 publications

(99 citation statements)

References 74 publications

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“…Diamantoulakis et al [12] have worked out some of the problems encountered by big data analytics in dynamic energy management, which appear to neatly summarize some of the problems to be expected when tackling industrial processes. The first concern is that traditional computer techniques cannot handle fast data processing, which is required for real-time monitoring, dynamic energy management and power flow optimization; the remedy is to employ efficient high performance computing techniques (virtualization, in-memory computing).…”

Section: Predictive Analyticsmentioning

confidence: 98%

Fuzzy Entropy Used for Predictive Analytics

Carlsson

Heikkilä

Mezei

2016

Fuzzy Logic in Its 50th Year

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Process interruptions in (very) large production systems are difficult to deal with. Modern processes are highly automated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from the very large sets of data. The sensors collect huge amounts of data but the failure events are few and infrequent and hard to find (and even harder to predict). In this article, our goal is to develop models for predictive maintenance in a big data environment. The purpose of feature selection in the context of predictive maintenance is to identify a small set of process diagnostics that are sufficient to predict future failures. We apply interval-valued fuzzy sets and various entropy measures defined on them to perform feature selection on process diagnostics. We show how these models can be utilized as the basis of decision support systems in process industries to aid predictive maintenance.

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Section: Predictive Analyticsmentioning

confidence: 98%

Fuzzy Entropy Used for Predictive Analytics

Carlsson

Heikkilä

Mezei

2016

Fuzzy Logic in Its 50th Year

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“…These cities are uniquely distinguished by provisions for intelligent data connectivity. Plenty of claims and proposals are found in the open domain which are being implemented independently in diverse ITS domains such as security in information management of smart grids [4], smart grid dynamic energy management (DEM) [21], agent based simulation of electric vehicles fleet charging strategies and several other areas [1]. However, the said methodologies are localized in silos and yet to realize the synchronization aspects, management tractability and commercial viability of the complete transportation system.…”

Section: Transport Oriented Smart City (Tosc) Architecturementioning

confidence: 99%

Federated Cloud Analytics Frameworks in Next Generation Transport Oriented Smart Cities (TOSCs) - Applications, Challenges and Future Directions

Hussain¹,

Alam²,

Beg³

2018

EAI Endorsed Transactions on Smart Cities

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Electric, plug-in electric and plug-in hybrid electric vehicles (xEVs) are receiving a global attention from automotive industries, vehicle vendors, R&D organizations, power sectors and policymakers in the intelligent transportation era. Penetration of xEV fleet into the contemporary charging infrastructure(s) in the absence of robust integration network imbalances the power grid and potentially jeopardize the execution of emerging distributed generation systems. However, smart grid technologies in collaboration with smart charging management strategies can circumvent such operational disparities, thus enabling a reliable, efficient, consistent and optimal electric energy management in the power system. This work employs the notion of Cloud of Things (CoT) to propose a comprehensive cloud aware Transport Oriented Smart City (TOSC) framework intended to provide intelligent solutions to the contemporary transportation infrastructures in the emerging sustainable smart cities. The proposed work also demonstrates a commercially viable vehicle to cloud (V2C) fleet charging framework for charging management of xEVs through micro grids/ smart grid. The unprecedented data breeding across V2C, cloud to grid (C2G) and grid to vehicle (G2V) bidirectional communication interfaces elucidates the need for computationally efficient analytics. A state-of-the-art Big-Data to Knowledge (B2K) workflow structure is thus proposed for translating the generated data into efficient knowledge for noble decisions and inferences. Finally, the substantial Mobility as a service (MaaS) adoption challenges and data science prospects are outlined along with the emerging technologies that can co-work with the proposed framework to ensure commercial viability and optimal implementation in emerging TOSCs.

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“…In such a conventional and, potentially outdated power grid system, there is little chance to optimally manage supply and demand effectively. In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged [1]. The Smart Grids European Technology Platform considers Smart Grid as an electricity network that can intelligently integrate the actions of all the connected users, generators, consumers, and those that do both, in order to efficiently deliver sustainable economic and secure electricity supply [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of Household Appliance Operation Cycles: A Case-Study Approach

Wang

Srinivasan

2015

Energies

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Abstract:In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged. With greater knowledge of operational characteristics of individual appliances, necessary automation control strategies can be developed in the Smart Grid to operate appliances in an efficient manner. This paper provides a way of classifying different operational cycles of a household appliance by introducing an unsupervised learning algorithm called k-means clustering. An intrinsic method known as silhouette coefficient was used to measure the classification quality. An identification process is also discussed in this paper to help users identify the operation mode each types of operation cycle stands for. A case study using a typical household refrigerator is presented to validate the proposed method. Results show that the proposed the classification and identification method can partition and identify different operation cycles adequately. Classification of operation cycles for such appliances is beneficial for Smart Grid as it provides a clear and convincing understanding of the operation modes for effective power management.

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Big Data Analytics for Dynamic Energy Management in Smart Grids

Cited by 263 publications

References 74 publications

Fuzzy Entropy Used for Predictive Analytics

Fuzzy Entropy Used for Predictive Analytics

Federated Cloud Analytics Frameworks in Next Generation Transport Oriented Smart Cities (TOSCs) - Applications, Challenges and Future Directions

Classification of Household Appliance Operation Cycles: A Case-Study Approach

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