Multi-agent system for energy consumption optimisation in higher education institutions

An Embedded Platform for Testbed Implementation of Multi-Agent System in Building Energy Management System

“…Load Agent Generator Agent Central Agent Other Agent [9] Load agent Generator agent -Storage agent [10] Heating agent, Cooling agent Electricity agent -- [11] Consumption agent, Load shifting agent Production agent Aggregation agent Storage agent [12] Local control agent, Load agent Switch agent Central coordinator agent - [13] Local zone agents, Zone agent [22] Local controller agents -Central coordinator agent - [23] Room agents -Coordinator agent -The energy management system proposed by [26] allows the users (Smarthome or Smartbuilding) to exchange the local jointly renewable energy resources. This approach is based on the decentralized algorithm to optimize the energy from the renewable resource, i.e., to be exchanged with the neighbors, and to optimize the energy of the distribution network, i.e., to be delivered to the network or extracted from the network.…”

“…The main objectives of the energy management systems [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] are usually to minimize the energy cost and/or maximize user comfort. In addition to these two optimization objectives, the objective function of maximizing the energy usage from the local renewable energy source is also employed [28,29].…”

“…The MAS-based BEMS could be classified into three groups, i.e., based-on: the number of criteria to be optimized, the optimization algorithm, and the implementation platform. In the first group, they are divided into single-objective optimization [10][11][12][13][14][15][16][17] and multi-objectives optimization [18][19][20][21][22][23]. Based on the optimization algorithm, they are divided into the conventional-based optimization techniques [10,11,13,15,17,21,23] and the artificial intelligent (AI)-based optimization techniques [12,14,16,[18][19][20]22].…”

“…A system to predict the energy demand of the building using the Artificial Neural Network (ANN) is proposed in [5]. The ANN model is trained using the dataset of monthly historical energy consumption of the building.Due to the distributed components (sensors, actuators, generators, loads) of the HEMS/BEMS, an intelligent multi-agent system (MAS) is widely adopted [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The MAS is a distributed control system where each agent works autonomously and coordinates with each other to achieve the global goal.…”

Optimisation, security, privacy and trust in e-business systems

“…Abraham, James and Yaacob show how parallelisation of the scheduler can create a far more efficient Grid system. The area of energy efficency is the subject of the second paper which is authored by Al-Daraiseh, El-Quwasmeh and Shah [2]. The authors have investigated how technology can aid energy reduction.…”