Developing A Multi Agent System in JADE for Micro Grids

Lakhina, Upasana; Elamvazuthi, Irraivan; Mériaudeau, Fabrice; Ramasamy, Gobbi; Jangra, Ajay

doi:10.1109/roma46407.2018.8986723

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…In this paper, JADE features are utilized on local and AWS servers. With the help of JADE, load balancing can be easily calculated on remote servers with reduced time, as JADE provides a set of valuable tools that help debug and deploy applications [19,20]. Agents are decisionmakers and adapt themselves to user needs.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Architecture for Local-Host and AWS with Multiagent System

Chawla¹,

Ahlawat²

2023

Intelligent Automation &Amp; Soft Computing

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Cloud computing has become one of the leading technologies in the world today. The benefits of cloud computing affect end users directly. There are several cloud computing frameworks, and each has ways of monitoring and providing resources. Cloud computing eliminates customer requirements such as expensive system configuration and massive infrastructure while improving dependability and scalability. From the user's perspective, cloud computing makes it easy to upload multiagents and operate on different web services. In this paper, the authors used a restful web service and an agent system to discuss, deployments, and analysis of load performance parameters like memory use, central processing unit (CPU) utilization, network latency, etc., both on localhost and an Amazon Web Service Elastic Cloud Computing (AWS-EC2) server. The Java Agent Development Environment (JADE) tool has been used to propose an architecture and conduct a comparative study on both local and remote servers. JADE is an open-source tool for maintaining applications on AWS infrastructure. The focus of the study should be to reduce the complexity and time of load performance parameters by using an agent system on a cloud server instead of establishing a massive infrastructure on a local system, even for a small application.

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Section: Related Workmentioning

confidence: 99%

A Proposed Architecture for Local-Host and AWS with Multiagent System

Chawla¹,

Ahlawat²

2023

Intelligent Automation &Amp; Soft Computing

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“…The multi agent system (MAS) is a system composed of a collection of autonomous software agents that work together to accomplish a particular task irrespective of their capabilities and knowledge [19,20,21]. Multi agent system provides an environment with basic functionalities for the execution of agents for specific task.…”

Section: Multi Agent Systemmentioning

confidence: 99%

Hybrid Approach for Wind Turbines Power Curve Modeling Founded on Multi-Agent System and Two Machine Learning Algorithms, K-Means Method and the K-Nearest Neighbors, in the Retrieve Phase of the Dynamic Case Based Reasoning

2022

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Wind turbine power curve (WTPC) plays an important role for energy assessment, power forecasting and condition monitoring. The WTPC captures the nonlinear relationship between wind speed and output power. Many modeling approaches have been proposed by researches to improve the WTPC model performance. In this paper, we present a hybrid approach of wind turbines power curve modeling based on Case Based Reasoning approach, multi agent system, the K-Means unsupervised machine learning method, and then the supervised machine learning algorithm, which is the K-Nearest Neighbors KNN method. The both of the Machine Learning algorithms, K-means and KNN, are used in the retrieve step of the Dynamic Case Based Reasoning (DCBR) cycle to facilitate the search of wind turbines with similar characteristics to our target case. These wind turbines are first grouped into homogeneous classes and then sorted on the basis of a feature similarity measure using the K-Nearest Neighbors supervised machine learning method. Finally, a set of WTPC with similar characteristics of the target case are proposed.

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Simulation of Household Appliances with Energy Disaggrigation using Deep Learning Technique

Nandish

Pushparajesh

2021

2021 International Conference on Computational Performance Evaluation (ComPE)

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Developing A Multi Agent System in JADE for Micro Grids

Cited by 4 publications

References 8 publications

A Proposed Architecture for Local-Host and AWS with Multiagent System

A Proposed Architecture for Local-Host and AWS with Multiagent System

Hybrid Approach for Wind Turbines Power Curve Modeling Founded on Multi-Agent System and Two Machine Learning Algorithms, K-Means Method and the K-Nearest Neighbors, in the Retrieve Phase of the Dynamic Case Based Reasoning

Simulation of Household Appliances with Energy Disaggrigation using Deep Learning Technique

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