Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

Bhattarai, Bishnu P.; Paudyal, Sumit; Luo, Yanhong; Mohanpurkar, Manish; Cheung, Kwok W.; Tonkoski, Reinaldo; Hovsapian, Rob; Myers, Kurt S.; Zhang, Rui; Zhao, Power Qing; Manic, Milos; Zhang, Song; Zhang, Xiaping

doi:10.1049/iet-stg.2018.0261

Cited by 182 publications

(75 citation statements)

References 147 publications

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“…Buildings data are heterogeneous, often dispersed in non-interoperable silos, with varying resolution, mostly asynchronous and are stored in different formats (raw or processed) at various locations [14]. Individual devices and functional units generate thousands of terabytes data annually and building-related stakeholders must handle millions of terabytes of data [15], which continues to increase over time.…”

Section: Introductionmentioning

confidence: 99%

Big Data for Energy Management and Energy-Efficient Buildings

Marinakis

2020

Energies

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European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.

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Section: Introductionmentioning

confidence: 99%

Big Data for Energy Management and Energy-Efficient Buildings

Marinakis

2020

Energies

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“…To help the integration of DRESs, 200 million smart meters will be invested in E.U. by 2020 [3], which facilitates increasing number of consumers to produce or store electricity at home through solar panels [4], electric vehicles [5], and batteries [6]. The role of prosumers is formed when consumers actively manage their own electricity generation and consumption [7] relying on smart meters and these technologies.…”

Section: Introduction a Motivation And Backgroundmentioning

confidence: 99%

A Blockchain-Based Peer-to-Peer Trading Scheme Coupling Energy and Carbon Markets

Hua

Sun

2019

2019 International Conference on Smart Energy Systems and Technologies (SEST)

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Climate change enforces the integration of distributed renewable energy sources and development of carbon price scheme. Whilst the energy is traded among distributed prosumers, the carbon responsibilities and corresponding allowances trading need to be transferred from large-scale energy suppliers to prosumers. During this transformation, the issues of energy imbalance, carbon reduction imbalance, and residential privacy leakage in centralised trading market present serious challenges. In this paper, we propose a fully decentralised blockchain-based peer-to-peer trading scheme coupling energy and carbon markets. We implement pay-to-public-key-hash with multiple signatures as a transaction standard to realise a more secure transaction and reduced storage burden of senders. A script is hashed during the wallet address generation for each new transaction to protect residential privacy. A novel carbon accounting method and corresponding incentive mechanism for carbon reduction are designed to evaluate emission behaviours of distributed prosumers. Case studies demonstrate that the proposed scheme leads a reduced costs and carbon emissions compared to centralised trading systems and existing blockchainbased trading schemes. Index Terms-blockchain, decentralized energy trading, low carbon, distributed energy sources, smart grids.

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“…At the same time, they provide quality power by allowing the power system to check consistently the grid's condition and user patterns as well as for any possible system flaws and providing alternative solutions in some cases. Such huge heterogeneous data combination [16][17][18][19][20] can be quite effective for the power grid when used along with the data obtained from the operational simulations using them. In general, a huge volume (terabytes) of data flows into these modern or smart grids annually [21][22][23][24][25][26][27][28][29] through various types of power network elements (e.g., sensors, smart equipment, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…In general, a huge volume (terabytes) of data flows into these modern or smart grids annually [21][22][23][24][25][26][27][28][29] through various types of power network elements (e.g., sensors, smart equipment, etc.) that constantly collect the data generated by geographic information systems, SCADA systems, or weather and traffic information systems or from social networks [17][18][19][20]. The development of information and communication technologies based on the advance of hardware and software creates new values across various fields of society in conjunction with the Big Data era [30].…”

Section: Introductionmentioning

confidence: 99%

A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

Jung

2019

Sustainability

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This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

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Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions

Cited by 182 publications

References 147 publications

Big Data for Energy Management and Energy-Efficient Buildings

Big Data for Energy Management and Energy-Efficient Buildings

A Blockchain-Based Peer-to-Peer Trading Scheme Coupling Energy and Carbon Markets

A Novel on Transmission Line Tower Big Data Analysis Model Using Altered K-means and ADQL

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