A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

García, Óscar; Prieto, Javier; Alonso, Ricardo S.; Corchado, Juan M.

doi:10.3390/s17081749

Cited by 31 publications

(17 citation statements)

References 59 publications

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“…Multi-agent systems are often applied to the field of building automation, due to their capacity to deal with more complex systems. Diverse HVAC process management systems have been developed by making use of the advantages provided by multi-agent systems, with the aim of optimizing energy consumption [ 47 , 48 , 49 , 50 ]. Wang et al used a multi agent system to develop a control system with an intelligent optimizer.…”

Section: Related Workmentioning

confidence: 99%

Energy Optimization Using a Case-Based Reasoning Strategy

González‐Briones

Prieto

Prieta

et al. 2018

Sensors

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124

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At present, the domotization of homes and public buildings is becoming increasingly popular. Domotization is most commonly applied to the field of energy management, since it gives the possibility of managing the consumption of the devices connected to the electric network, the way in which the users interact with these devices, as well as other external factors that influence consumption. In buildings, Heating, Ventilation and Air Conditioning (HVAC) systems have the highest consumption rates. The systems proposed so far have not succeeded in optimizing the energy consumption associated with a HVAC system because they do not monitor all the variables involved in electricity consumption. For this reason, this article presents an agent approach that benefits from the advantages provided by a Multi-Agent architecture (MAS) deployed in a Cloud environment with a wireless sensor network (WSN) in order to achieve energy savings. The agents of the MAS learn social behavior thanks to the collection of data and the use of an artificial neural network (ANN). The proposed system has been assessed in an office building achieving an average energy savings of 41% in the experimental group offices.

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Section: Related Workmentioning

confidence: 99%

Energy Optimization Using a Case-Based Reasoning Strategy

González‐Briones

Prieto

Prieta

et al. 2018

Sensors

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124

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“…Padmanabh et al [ 11 ] focused on office buildings and regulated both air and lighting when people were present in meeting rooms. Other works such as the one conducted by García et al [ 12 ] optimized energy consumption by using gamification techniques together with the information collected through a Wireless Sensor Network (WSN) [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Agreement Technologies for Energy Optimization at Home

González‐Briones

Chamoso

Prieta

et al. 2018

Sensors

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Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%.

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“…However, this work was a theoretical study in which the modifications that the framework had to have for its adaptation to real homes were proposed. It is already in [81], where the use of the CAFCLA framework as a recommendation system in homes is evaluated. One of the points where the system evolved most was the integration of a Real-Time Localization System in to Wireless Sensor Networks, making it possible to develop applications that work under the umbrella of Social Computing.…”

Section: Wsn Management Mas For Optimization Decision-makingmentioning

confidence: 99%

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

et al. 2018

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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.

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A Framework to Improve Energy Efficient Behaviour at Home through Activity and Context Monitoring

Cited by 31 publications

References 59 publications

Energy Optimization Using a Case-Based Reasoning Strategy

Energy Optimization Using a Case-Based Reasoning Strategy

Agreement Technologies for Energy Optimization at Home

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

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