A Fog Computing Architecture for Energy Demand Scheduling in Smart Grid

2019 ◽  
Author(s):  
Samira Chouikhi ◽  
Leila Merghem-Boulahia ◽  
Moez Esseghir
Keyword(s):  
Smart Grid ◽  
Energy Demand ◽  
Fog Computing ◽  
Computing Architecture

scholarly journals New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1217 ◽  
Author(s):  
İsmail ÇAVDAR ◽  
Vahid FARYAD
Keyword(s):  
Neural Network ◽  
Smart Grid ◽  
Convolutional Neural Network ◽  
Energy Demand ◽  
Short Term Memory ◽  
Fog Computing ◽  
Processing Unit ◽  
Demand Side ◽  
Energy Disaggregation ◽  
Load Monitoring

Energy management technology of demand-side is a key process of the smart grid that helps achieve a more efficient use of generation assets by reducing the energy demand of users during peak loads. In the context of a smart grid and smart metering, this paper proposes a hybrid model of energy disaggregation through deep feature learning for non-intrusive load monitoring to classify home appliances based on the information of main meters. In addition, a deep neural model of supervised energy disaggregation with a high accuracy for giving awareness to end users and generating detailed feedback from demand-side with no need for expensive smart outlet sensors was introduced. A new functional API model of deep learning (DL) based on energy disaggregation was designed by combining a one-dimensional convolutional neural network and recurrent neural network (1D CNN-RNN). The proposed model was trained on Google Colab’s Tesla graphics processing unit (GPU) using Keras. The residential energy disaggregation dataset was used for real households and was implemented in Tensorflow backend. Three different disaggregation methods were compared, namely the convolutional neural network, 1D CNN-RNN, and long short-term memory. The results showed that energy can be disaggregated from the metrics very accurately using the proposed 1D CNN-RNN model. Finally, as a work in progress, we introduced the DL on the Edge for Fog Computing non-intrusive load monitoring (NILM) on a low-cost embedded board using a state-of-the-art inference library called uTensor that can support any Mbed enabled board with no need for the DL API of web services and internet connectivity.

Machine Learning Model for the Detection of Electric Energy Fraud using an Edge-Fog Computing Architecture

2020 ◽  
Author(s):  
Juan C. Olivares-Rojas ◽  
Enrique Reyes-Archundia ◽  
Noel E. Rodriiguez-Maya ◽  
Jose A. Gutierrez-Gnecchi ◽  
Ismael Molina-Moreno ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fog Computing ◽  
Electric Energy ◽  
Learning Model ◽  
Computing Architecture ◽  
Machine Learning Model

scholarly journals Digital Certificate Verification Scheme for Smart Grid using Fog Computing (FONICA)

2021 ◽  
Vol 13 (5) ◽  
pp. 2549
Author(s):  
Shahid Mahmood ◽  
Moneeb Gohar ◽  
Jin-Ghoo Choi ◽  
Seok-Joo Koh ◽  
Hani Alquhayz ◽  
...  
Keyword(s):  
Smart Grid ◽  
Energy Efficient ◽  
Fog Computing ◽  
Security Threats ◽  
Certification Authority ◽  
Certificate Revocation ◽  
Grid Networks ◽  
Digital Certificates ◽  
Attack Surface ◽  
Energy Network

Smart Grid (SG) infrastructure is an energy network connected with computer networks for communication over the internet and intranets. The revolution of SGs has also introduced new avenues of security threats. Although Digital Certificates provide countermeasures, however, one of the issues that exist, is how to efficiently distribute certificate revocation information among Edge devices. The conventional mechanisms, including certificate revocation list (CRL) and online certificate status protocol (OCSP), are subjected to some limitations in energy efficient environments like SG infrastructure. To address the aforementioned challenges, this paper proposes a scheme incorporating the advantages and strengths of the fog computing. The fog node can be used for this purpose with much better resources closer to the edge. Keeping the resources closer to the edge strengthen the security aspect of smart grid networks. Similarly, a fog node can act as an intermediate Certification Authority (CA) (i.e., Fog Node as an Intermediate Certification Authority (FONICA)). Further, the proposed scheme has reduced storage, communication, processing overhead, and latency for certificate verification at edge devices. Furthermore, the proposed scheme reduces the attack surface, even if the attacker becomes a part of the network.

Attaining Sustainability over Fog Computing Architecture

2021 ◽  
Author(s):  
Suresh Shanmugasundaram ◽  
Divya Preya Chidambaram
Keyword(s):  
Fog Computing ◽  
Computing Architecture

scholarly journals Fog Computing in the context of Smart Home, voice assistant and the future of IoT

2020 ◽  
Vol 21 (1) ◽  
pp. 6-12
Author(s):  
Javier Pinzón Castellanos ◽  
Miguel Antonio Cadena Carter
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Data Transmission ◽  
Smart Home ◽  
Fog Computing ◽  
Relevant Information ◽  
Raspberry Pi ◽  
Thermal Sensors ◽  
Computing Architecture ◽  
The Future

Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer. This study used a Raspberry Pi V3 board connected to end control elements such as servomotors and relays, indicators and thermal sensors. All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission, because it only receives relevant information for analysis.

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.

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