Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Abstract

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

“…While Smart Grids were focused solely on the area of electricity, the term Smart Energy also encompasses HVAC (heating, ventilation, and air-conditioning) systems [5], and the intelligent use of energy in Industry 4.0 [6], transportation [7], public buildings [8,9], and homes [10,11]. Smart Energy solutions involve the use of different disruptive technologies, including Artificial Intelligence (such as case-based reasoning systems [12], multi-agent systems) [13], Deep Learning [14]; Distributed • Layer 1 (IoT + Sensors): The first layer is formed by the IoT devices (sensors, smart-phones, relays, etc.) and the users of the system they interact with.…”

“…The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provides multiple solutions to each of its application areas.…”

Hybrid job offer recommender system in a social network

“…Agent‐based systems allow the entities that constitute them to communicate, coordinate, interact, and cooperate with each other for the purpose of performing the activities for which they have been designed. They have been applied in e‐commerce systems (Briones, Chamoso, & Barriuso, ), in the development of smart contracts in blockchain (Casado‐Vara, González‐Briones, Prieto, & Corchado, ), in architectures for optimized energy consumption in intelligent buildings (González‐Briones, De La Prieta, Mohamad, Omatu, & Corchado, ), in the development of management architectures for smart cities (Chamoso, González‐Briones, Rodríguez, & Corchado, ), or in incentive systems for recycling processes in smart cities (González‐Briones et al, ).…”

Predictive Energy Control Strategy for Peak Shaving and Shifting Using BESS and PV Generation Applied to the Retail Sector

“…The performed energy and economic simulations based on the experimental data collected in an Italian shopping mall clearly highlight the benefits in terms of energy and economic savings. Moreover, the reported results lead to the conclusion that BESS management, photovoltaic (PV) generation, and peak switch strategies can have a reasonable pay-back investment time even for buildings with a large energy demand.reason, a variety of smart building energy management systems (BEMS), e.g., based on a multi-agent architecture (MAS) have been proposed in the scientific literature [3]. For instance, a specific strategy based on case-reasoning is presented in [4], where it is clearly explained that the reduction of building consumption depends not only on a proper coordination of devices (or agents), but it should take into account human behavior as well.…”

“…reason, a variety of smart building energy management systems (BEMS), e.g., based on a multi-agent architecture (MAS) have been proposed in the scientific literature [3]. For instance, a specific strategy based on case-reasoning is presented in [4], where it is clearly explained that the reduction of building consumption depends not only on a proper coordination of devices (or agents), but it should take into account human behavior as well.…”