scholarly journals Research on Multi-Agent Systems in a Smart Small Grid for Resource Apportionment and Planning

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Zhixian Yang ◽  
Kshuangchen Fu ◽  
Jhon Paul
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Energy Saving ◽  
Behavior Pattern ◽  
Negotiation Process ◽  
User Preference ◽  
Multi Agent Systems ◽  
Negative Change ◽  
Energy Consumption Optimization ◽  
Multi Agent

With the advancement in the technology, deployment of sensors in the industrial or public building is increasing rapidly. The basic aim is to obtain the data from the environment and decision making to the energy saving. The activities caused by the human results the undergoing negative change in the environment. There are many techniques available for decision making and consider the environmental factors solely which cause the energy consumption. However, user’s preferences are not adapted by the systems, but at energy consumption optimization, these systems are very successful. The end-users use the system which considers the factors and their wellbeing are get affected. The distributed generation is incorporated by the Smart Small Grid (SSG), communication network and the sensors for the more reliable, flexible and efficient grid. The energy saving system is presented in this paper which also adapts to the inhabitants preferences apart from environmental conditions consideration. The architecture of Multi-Agent System (MAS) and the agents are utilized for negotiation process performance between the users comfort preferences and optimization degree that according to these preferences, achievement of system is done. The energy consumption of 40% is obtained and in the inhabitants' behavior pattern, the algorithm was specialized. The 16.89% of reduction is obtained by the existing system and it was focused to obtain the agreement between the system and users for user preference satisfaction and the energy optimization is also performed at the same time.

Aspect-Oriented Recommender Systems

2013 ◽  
pp. 55-72
Author(s):  
Punam Bedi ◽  
Sumit Kr Agarwal
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Recommender System ◽  
User Preference ◽  
Preference Learning ◽  
Decision Making Process ◽  
Design Issues ◽  
Aspect Oriented Programming ◽  
Multi Agent ◽  
Oriented Approach

Recommender systems are widely used intelligent applications which assist users in a decision-making process to choose one item amongst a potentially overwhelming set of alternative products or services. Recommender systems use the opinions of members of a community to help individuals in that community by identifying information most likely to be interesting to them or relevant to their needs. Recommender systems have various core design crosscutting issues such as: user preference learning, security, mobility, visualization, interaction etc that are required to be handled properly in order to implement an efficient, good quality and maintainable recommender system. Implementation of these crosscutting design issues of the recommender systems using conventional agent-oriented approach creates the problem of code scattering and code tangling. An Aspect-Oriented Recommender System is a multi agent system that handles core design issues of the recommender system in a better modular way by using the concepts of aspect oriented programming, which in turn improves the system reusability, maintainability, and removes the scattering and tangling problems from the recommender system.

scholarly journals Definition of a Framework for Acquiring and Acquisition Subprocesses in a Collective Knowledge Processing in the Integrated Management Information System

2019 ◽  
Vol 06 (03) ◽  
pp. 257-272
Author(s):  
Marcin Hernes ◽  
Ngoc Thanh Nguyen
Keyword(s):  
Decision Making ◽  
Integrated Management ◽  
Business Organization ◽  
Business Organizations ◽  
Multi Agent Systems ◽  
Collective Knowledge ◽  
Management Information ◽  
Efficient Operation ◽  
Multi Agent ◽  
Definition Of

Efficient operation of the integrated management information systems (IMISs), especially multi-agent systems, is related to their ability to automatically process collective knowledge. On the basis of this knowledge the decision-making process is realized in the business organizations. This paper presents issues related to framework for acquiring and acquisition subprocesses in a collective knowledge of business organization processing in IMIS. The main novelty of the developed framework is the coverage of all the areas of operation of an organization. Additionally, the inter-area knowledge for automatic strategic-level decision-making has been taken into consideration. The main improvements of this framework are that it allows for processing of the whole collective knowledge of business organization and it can be directly implemented in IMIS.

An intelligent system for energy management in smart cities based on big data and ontology

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zaoui Sayah ◽  
Okba Kazar ◽  
Brahim Lejdel ◽  
Abdelkader Laouid ◽  
Ahmed Ghenabzia
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Energy Saving ◽  
Intelligent System ◽  
Smart Cities ◽  
Semantic Integration ◽  
Saving Energy ◽  
Content Type ◽  
Multi Agent ◽  
Reduce Energy Consumption

PurposeThis research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.Design/methodology/approachThis study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.FindingsThrough this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.Practical implicationsThe significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.Originality/valueThis work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

scholarly journals Synchronization of Local Plans for Cooperative Distributed Decision-Making

2021 ◽  
Author(s):  
Maximilian Kloock ◽  
Bassam Alrifaee
Keyword(s):  
Decision Making ◽  
Predictive Control ◽  
Local System ◽  
Multi Agent Systems ◽  
Local Planning ◽  
Distributed Decision Making ◽  
Planning Step ◽  
Multi Agent ◽  
Cooperative Decision Making ◽  
Distributed Decision

In cooperative decision-making, agents locally plan for a subset of all agents. Due to only local system knowledge of the agents, these local plans may be inconsistent to local plans of other agents. This inconsistency leads to infeasibility of the plans. This article introduces an algorithm for synchronizing local plans for cooperative distributed decision-making of multi-agent systems. The algorithm consists of two iterative steps: planning and synchronization. In the local planning step, the agents compute local decisions, referred to as plans. Subsequently, consistency of the local plans across agents is achieved using synchronization. The synchronized plans act as reference decisions to the local planning step in the next iteration. In each iteration, the local planning guarantees locally feasible plans, while the synchronization guarantees globally consistent plans in that iteration. The algorithm converges to globally feasible decisions if the coupling topology is feasible. We introduce requirements for the coupling topology to achieve convergence to globally feasible decisions and present the algorithm using a model predictive control example. Our evaluations with car-like robots show that feasible decisions are achieved.

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