IoT Big Data Analytics with Fog Computing for Household Energy Management in Smart Grids

2018 ◽  
pp. 13-22 ◽  
Author(s):  
Shailendra Singh ◽  
Abdulsalam Yassine
Keyword(s):  
Big Data ◽  
Energy Management ◽  
Smart Grids ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Household Energy

scholarly journals Big Data Analytics for Dynamic Energy Management in Smart Grids

2015 ◽  
Vol 2 (3) ◽  
pp. 94-101 ◽  
Author(s):  
Panagiotis D. Diamantoulakis ◽  
Vasileios M. Kapinas ◽  
George K. Karagiannidis
Keyword(s):  
Big Data ◽  
Energy Management ◽  
Smart Grids ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Dynamic Energy Management

A smart home energy management system using IoT and big data analytics approach

2017 ◽  
Vol 63 (4) ◽  
pp. 426-434 ◽  
Author(s):  
A.R. Al-Ali ◽  
Imran A. Zualkernan ◽  
Mohammed Rashid ◽  
Ragini Gupta ◽  
Mazin Alikarar
Keyword(s):  
Big Data ◽  
Energy Management ◽  
Management System ◽  
Data Analytics ◽  
Smart Home ◽  
Big Data Analytics ◽  
Energy Management System ◽  
Home Energy Management ◽  
Home Energy Management System

Fog Computing Realization for Big Data Analytics

2019 ◽  
pp. 259-290 ◽  
Author(s):  
Farhad Mehdipour ◽  
Bahman Javadi ◽  
Aniket Mahanti ◽  
Guillermo Ramirez-Prado
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics

Smart Grids and Big Data Analytics for Smart Cities

2021 ◽  
Author(s):  
Chun Sing Lai ◽  
Loi Lei Lai ◽  
Qi Hong Lai
Keyword(s):  
Big Data ◽  
Smart Grids ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics

scholarly journals A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

2021 ◽  
Vol 13 (23) ◽  
pp. 13322
Author(s):  
Vinoth Kumar Ponnusamy ◽  
Padmanathan Kasinathan ◽  
Rajvikram Madurai Elavarasan ◽  
Vinoth Ramanathan ◽  
Ranjith Kumar Anandan ◽  
...  
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Data Management ◽  
Smart Grids ◽  
Data Analytics ◽  
Management Practices ◽  
Research Work ◽  
Big Data Analytics ◽  
Energy Data ◽  
The Sustainable Development

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

IoT Big Data Architectures, Approaches, and Challenges

2021 ◽  
pp. 227-250
Author(s):  
David Sarabia-Jácome ◽  
Regel Gonzalez-Usach ◽  
Carlos E. Palau
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Fog Computing ◽  
Big Data Analytics ◽  
Late Response ◽  
Reference Architecture ◽  
Comprehensive Overview ◽  
High Bandwidth ◽  
Iot Devices ◽  
Application Developers

The internet of things (IoT) generates large amounts of data that are sent to the cloud to be stored, processed, and analyzed to extract useful information. However, the cloud-based big data analytics approach is not completely appropriate for the analysis of IoT data sources, and presents some issues and limitations, such as inherent delay, late response, and high bandwidth occupancy. Fog computing emerges as a possible solution to address these cloud limitations by extending cloud computing capabilities at the network edge (i.e., gateways, switches), close to the IoT devices. This chapter presents a comprehensive overview of IoT big data analytics architectures, approaches, and solutions. Particularly, the fog-cloud reference architecture is proposed as the best approach for performing big data analytics in IoT ecosystems. Moreover, the benefits of the fog-cloud approach are analyzed in two IoT application case studies. Finally, fog-cloud open research challenges are described, providing some guidelines to researchers and application developers to address fog-cloud limitations.

scholarly journals Generation of dynamic energy management using data mining techniques basing on big data analytics isssues in smart grids

2018 ◽  
Vol 7 (2.26) ◽  
pp. 85
Author(s):  
Dr E. Laxmi Lydia ◽  
B Prasanna Kumar ◽  
D Ramya
Keyword(s):  
Big Data ◽  
Smart Grid ◽  
Data Processing ◽  
Energy Management ◽  
Electricity Market ◽  
Smart Grids ◽  
Data Analytics ◽  
Performance Estimation ◽  
Smart Meters ◽  
Dynamic Energy Management

The Optimal bidirectional flow of the electric power and the communicational data between suppliers and consumers are greatly enabled by the Smart Electricity in Grid. Reliable and Feasible micro energy generated due to Dynamic Energy Management (DEM) and the electricity market by consumers and suppliers. The smart grid features ICCM, aims to bring out the power at reduced cost. Powerful and practical DEM relies on load and sustainable production. Smart meters attain the huge data quantity through practical methods and solutions in this real world working. Smart Grids are enhanced by the operations such as data analytics, giving out high performance estimation, Adequate data network management and cloud computing. This paper aims focusthe issuesin big data and challenges experienced by the Dynamic Energy Management signed in Smart Grid. A detail explanation of data processing techniques that are mostly implemented and It also provides a brief description of the most commonly used data processing methods and recommended proposes a upcoming future directional research in thefield. 

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