Home energy management system based on daily demand prediction and ZigBee network

García, Emilio José; Flores-Arias, J.M.; Chen, Aihou; Quiles-Latorre, Francisco Javier; Bellido-Outeiriño, F.J.

doi:10.1109/icce.2015.7066428

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…In this case, wide coverage is no longer required and ease of implementation and scalability are the important required characteristics that define the selected communication protocol. ZigBee is a very good candidate for smarthomes [14,15]. In line with the development of MG, their low level control structures became less communication dependent and more reliant on local measurements.…”

Section: Communication System For Residential Mg Managementmentioning

confidence: 99%

Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques

Arbab-Zavar

Sharkh

García

et al. 2022

Sensors

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A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failures. This paper discusses the effects of communication link failures on MG control and management and proposes solutions based on enhancing message content to mitigate their detritus impact. In order to achieve this goal, generation and consumption forecasting using deep learning (DL) methods at the next time steps is used. The architecture of an energy management system (EMS) and an energy storage system (ESS) that are able to operate in coordination is introduced and evaluated by simulation tests, which show promising results and illustrate the efficacy of the proposed methods. It is important to mention that, in this paper, three dissimilar topics namely MG control/management, DL-based forecasting, and D2D communication architectures are employed and this combination is proven to be capable of achieving the aforesaid objective.

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Section: Communication System For Residential Mg Managementmentioning

confidence: 99%

Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques

Arbab-Zavar

Sharkh

García

et al. 2022

Sensors

Self Cite

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“…Also, the research work in Ref. [19] describes the development of an application to control the in-home energy consumption. The proposed application performs the management of energy by means of a wireless personal network based on ZigBee standard and predicts the demand using stochastic models hosted on a Web Server.…”

Section: Related Workmentioning

confidence: 99%

Integrated Management of Energy, Wellbeing and Health in the Next Generation of Smart Homes

Jaouhari

García

Anvari‐Moghaddam

et al. 2019

Sensors

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This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).

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“…Tradicionalmente existían sistemas que utilizaban protocolos de comunicación como TRF1000 o el CC1000 para comunicar los nodos de la red, pero en la actualidad para realizar aplicaciones a niveles de redes sus utilizan protocolos basados en la IEEE 802.15.4 [39]. Además algunas otras ventajas que presentan estas redes son mayor facilidad en las instalaciones, nos permiten tener un sistema de detección de errores, poseen un mayor alcance, una mayor velocidad de los flujos de información, entre otras muchas otras [40].…”

Section: A Infraestructura De Medición Avanzada En Hogares Inteligentesunclassified

Cobertura máxima de redes de sensores inalámbricos para un sistema de gestión de energía en hogares inteligentes

2016

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Resumen− Este artículo se desarrolla con base en redes de sensores sobre Smart Grids, en conjunto con una metodología de gestión apropiada que nos permita analizar y comprobar mediante una simulación el comportamiento de estas redes inalámbricas de una manera interna, dentro de una vivienda u hogar inteligente, teniendo en cuenta varios parámetros o restricciones como son la distancia de cobertura, la capacidad y el porcentaje de cobertura. Para ello se describe la infraestructura de redes inteligentes y se plantea un modelo matemático para minimizar la distancia de los sensores a los puntos de acceso dentro del área considerada. Además está enfocado en el diseño de estas redes inteligentes, donde se realiza una optimización de los diferentes puntos factibles, con lo que se logra una máxima cobertura de los sensores para implementar un adecuado sistema de gestión de energía en hogares inteligentes, basado en un servicio fiable de la conectividad de la red.Palabras clave− Redes de Sensores Inalámbricos, Hogares Inteligentes, Cobertura, Infraestructura de Medición Avanzada, Interior, Optimización.Abstract− This article was developed based on sensor networks for Smart Grids, together with appropriate management methodology which it allows us to analyze and check by a simulation the behavior of these wireless networks in an internal way, inside of a dwelling or smart home, knowing that we use different parameters or restrictions like the coverage distance, the capacity and the percentage of coverage. For this we described the intelligent network infrastructure and we propose a mathematical model to minimize the distance from the sensors to the access points inside the considered area. Additionally, this paper is focused on the design of these smart grids. An optimization of the different feasible points is presented, obtaining a maximum coverage of the sensors, in order to implement an appropriate energy management system in smart homes, based on reliable service network connectivity.

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Home energy management system based on daily demand prediction and ZigBee network

Cited by 9 publications

References 8 publications

Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques

Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques

Integrated Management of Energy, Wellbeing and Health in the Next Generation of Smart Homes

Cobertura máxima de redes de sensores inalámbricos para un sistema de gestión de energía en hogares inteligentes

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