Smart Grid Demand-Side Management by Machine Learning

2020 ◽  
pp. 633-644
Author(s):  
Rebeka Bhattacharyya ◽  
Avijit Bhattacharyya
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Demand Side Management ◽  
Demand Side

Secure and resilient demand side management engine using machine learning for IoT-enabled smart grid

2020 ◽  
Vol 62 ◽  
pp. 102370 ◽  
Author(s):  
Muhammad Babar ◽  
Muhammad Usman Tariq ◽  
Mian Ahmad Jan
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Demand Side Management ◽  
Demand Side

scholarly journals A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

Energies ◽  
2017 ◽  
Vol 10 (3) ◽  
pp. 319 ◽  
Author(s):  
Nadeem Javaid ◽  
Sakeena Javaid ◽  
Wadood Abdul ◽  
Imran Ahmed ◽  
Ahmad Almogren ◽  
...  
Keyword(s):  
Smart Grid ◽  
Optimization Algorithm ◽  
Demand Side Management ◽  
Heuristic Optimization ◽  
Demand Side

Genetic Algorithm Based Demand Side Management for Smart Grid

2017 ◽  
Vol 93 (2) ◽  
pp. 481-502 ◽  
Author(s):  
C. Bharathi ◽  
D. Rekha ◽  
V. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Smart Grid ◽  
Demand Side Management ◽  
Demand Side

scholarly journals Demand side management using the internet of energy based on LoRaWAN technology

2017 ◽  
Vol 2 (3) ◽  
pp. 112-119 ◽  
Author(s):  
Om-Kolsoom Shahryari ◽  
Amjad Anvari-Moghaddam ◽  
Shadi Shahryari
Keyword(s):  
Smart Grid ◽  
Long Range ◽  
Fog Computing ◽  
Demand Side Management ◽  
Wide Area ◽  
Area Network ◽  
Demand Side ◽  
Wide Area Network ◽  
Telecommunication System ◽  
Processing Level

The smart grid, as a communication network, allows numerous connected devices such as sensors, relays and actuators to interact and cooperate with each other. An Internet-based solution for electricity that provides bidirectional flow of information and power is internet of energy (IoE) which is an extension of smart grid concept. A large number of connected devices and the huge amount of data generated by IoE and issues related to data transmission, process and storage, force IoE to be integrated by cloud computing. Furthermore, in order to enhance the performance and reduce the volume of transmitted data and process information in an acceptable time, fog computing is suggested as a layer between IoE layer and cloud layer. This layer is used as a local processing level that leads to reduction in data transmissions to the cloud. So, it can save energy consumption used by IoE devices to transmit data into cloud because of a long range, low power, wide area and low bit rate wireless telecommunication system which is called LoRaWAN. All devices in fog domain are connected by long range wide area network (LoRa) into a smart gateway.  The gateway which bridges fog domain and cloud, is introduced for scheduling devices/appliances by creating a priority queue which can perform demand side management dynamically. The queue is affected by not only the consumer importance but also the consumer policies and the status of energy resources.

Sign in / Sign up

Export Citation Format

Share Document