Big Data Analytics for Price Forecasting in Smart Grids

Author(s):  
Kun Wang ◽  
Chenhan Xu ◽  
Song Guo
2021 ◽  
Author(s):  
Chun Sing Lai ◽  
Loi Lei Lai ◽  
Qi Hong Lai

2021 ◽  
Vol 13 (23) ◽  
pp. 13322
Author(s):  
Vinoth Kumar Ponnusamy ◽  
Padmanathan Kasinathan ◽  
Rajvikram Madurai Elavarasan ◽  
Vinoth Ramanathan ◽  
Ranjith Kumar Anandan ◽  
...  

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.


2019 ◽  
Vol 5 (1) ◽  
pp. 34-45 ◽  
Author(s):  
Kun Wang ◽  
Chenhan Xu ◽  
Yan Zhang ◽  
Song Guo ◽  
Albert Y. Zomaya

2013 ◽  
Vol 15 (4) ◽  
pp. 38-47 ◽  
Author(s):  
Yogesh Simmhan ◽  
Saima Aman ◽  
Alok Kumbhare ◽  
Rongyang Liu ◽  
Sam Stevens ◽  
...  

Author(s):  
Panagiotis D. Diamantoulakis ◽  
George K. Karagiannidis

2020 ◽  
pp. 833-854
Author(s):  
Md Muzakkir Hussain ◽  
M.M. Sufyan Beg ◽  
Mohammad Saad Alam ◽  
Shahedul Haque Laskar

Electric vehicles (EVs) are key players for transport oriented smart cities (TOSC) powered by smart grids (SG) because they help those cities to become greener by reducing vehicle emissions and carbon footprint. In this article, the authors analyze different use-cases to show how big data analytics (BDA) can play vital role for successful electric vehicle (EV) to smart grid (SG) integration. Followed by this, this article presents an edge computing model and highlights the advantages of employing such distributed edge paradigms towards satisfying the store, compute and networking (SCN) requirements of smart EV applications in TOSCs. This article also highlights the distinguishing features of the edge paradigm, towards supporting BDA activities in EV to SG integration in TOSCs. Finally, the authors provide a detailed overview of opportunities, trends, and challenges of both these computing techniques. In particular, this article discusses the deployment challenges and state-of-the-art solutions in edge privacy and edge forensics.


Sign in / Sign up

Export Citation Format

Share Document