Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Hossain, Eklas; Khan, Imtiaj; Un-Noor, Fuad; Sikander, Sarder Shazali; Sunny, Md Samiul Haque

doi:10.1109/access.2019.2894819

Cited by 368 publications

(189 citation statements)

References 197 publications

(178 reference statements)

Supporting

Mentioning

156

Contrasting

Unclassified

Order By: Relevance

“…More generally, the application of deep learning for refrigeration system control is limited, for example Hoang [31] purports gains of 7% in daily chiller energy efficiency by using deep learning opposed to rote physical control models. There have also been some studies focussing on load forecasting for home appliances and energy disaggregation including refrigerators [32,33,34]. Although quite a few studies exist in related areas of research, such as demand response algorithms for smart-grid ready residential buildings in [35] or demand response strategies in autonomous micro-grids in [36], there has been no studies specifically deploying deep learning for DSR control in food retailing refrigeration systems.…”

Section: Case Study -Demand Side Response Demonstrationmentioning

confidence: 99%

Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems

Onoufriou

Bickerton

Pearson

et al. 2019

Computers in Industry

View full text Add to dashboard Cite

Deep Learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite a few single node deep learning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete processing structure that can accommodate large scale data processing, version control, and deployment, all while staying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new, higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allow processes to be fed unique and transformed data at the point of need. This facilitates near real-time application and makes models available for further training or use at any node that has access to the database simultaneously. Nemesyst is well suited as an application framework for internet of things aggregated control systems, deploying deep learning techniques to optimise individual machines in massive networks. To demonstrate this framework, we adopted a case study in a novel domain; deploying deep learning to optimise the high speed control of electrical power consumed by a massive internet of things network of retail refrigeration systems in proportion to load available on the UK National Grid (a demand side response). The case study demonstrated for the first time in such a setting how deep learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative Adversarial Networks paired with Nemesyst, achieve compelling performance, whilst still being malleable to future adjustments as both the data and requirements inevitably change over time.

show abstract

Section: Case Study -Demand Side Response Demonstrationmentioning

confidence: 99%

Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems

Onoufriou

Bickerton

Pearson

et al. 2019

Computers in Industry

View full text Add to dashboard Cite

show abstract

“…Challenges of big data for electrical power and energy industry include privacy, data mining, integration of data, cyber security, and demand prediction through analytics processing in smart grid (SG) applications, data quality and cost balance, industrial fault diagnosis using big data and quantum cryptography for data security in smart grids. Some challenges lying ahead in the terms of SG big data technology includes multisource data integration and storage, real-time data processing technology, data compression, big data visualization technology, and data privacy and security [34]. State of art, current status and recent developments of big data are: a.…”

Section: Challenges and Opportunities Of Big Data And Machine Learninmentioning

confidence: 99%

A survey of big data and machine learning

Salkuti¹

2020

IJECE

View full text Add to dashboard Cite

This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.

show abstract

“…BPSO is a population-based heuristic optimization technique used in micro grid for load scheduling [69][70][71] in which each possible solution in entire search space is represented by a particle. Initially, each particle is assigned a random position and velocity.…”

Section: Optimal Load Scheduling Using Bpsomentioning

confidence: 99%

A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

et al. 2019

View full text Add to dashboard Cite

The smart grid (SG) has emerged as a key enabling technology facilitating the integration of variable energy resources with the objective of load management and reduced carbon-dioxide (CO 2 ) emissions. However, dynamic load consumption trends and inherent intermittent nature of renewable generations may cause uncertainty in active resource management. Eventually, these uncertainties pose serious challenges to the energy management system. To address these challenges, this work establishes an efficient load scheduling scheme by jointly considering an on-site photo-voltaic (PV) system and an energy storage system (ESS). An optimum PV-site matching technique was used to optimally select the highest capacity and lowest cost PV module. Furthermore, the best-fit of PV array in regard with load is anticipated using least square method (LSM). Initially, the mathematical models of PV energy generation, consumption and ESS are presented along with load categorization through Zero and Finite shift methods. Then, the final problem is formulated as a multiobjective optimization problem which is solved by using the proposed Dijkstra algorithm (DA). The proposed algorithm quantifies day-ahead electricity market consumption cost, used energy mixes, curtailed load, and grid imbalances. However, to further analyse and compare the performance of proposed model, the results of the proposed algorithm are compared with the genetic algorithm (GA), binary particle swarm optimization (BPSO), and optimal pattern recognition algorithm (OPRA), respectively. Simulation results show that DA achieved 51.72% cost reduction when grid and renewable sources are used. Similarly, DA outperforms other algorithms in terms of maximum peak to average ratio (PAR) reduction, which is 10.22%.

show abstract

Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

Cited by 368 publications

References 197 publications

Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems

Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems

A survey of big data and machine learning

A Residential Load Scheduling with the Integration of On-Site PV and Energy Storage Systems in Micro-Grid

Contact Info

Product

Resources

About