A Meta-Heuristic Home Energy Management System

Zafar, Asim; Shah, Samia; Khalid, Rabiya; Hussain, Sardar Mehboob; Rahim, Hassan; Javaid, Nadeem

doi:10.1109/waina.2017.118

Cited by 25 publications

(9 citation statements)

References 19 publications

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“…Furthermore, Zafar et al measured the performance of a HEMS using three meta-heuristic optimization techniques, namely, the HS algorithm, BFO algorithm, and EDE algorithm. 207 The performance of the algorithms was measured in terms of PAR reduction and energy cost minimization. The HAS algorithm achieved higher energy cost and PAR reduction than the BFO algorithm.…”

Section: Optimization Models With Emerging Techniques and Methodsmentioning

confidence: 99%

“…The BFO and GWO reduced the energy cost by 45% and 55%, respectively. Furthermore, Zafar et al measured the performance of a HEMS using three meta‐heuristic optimization techniques, namely, the HS algorithm, BFO algorithm, and EDE algorithm 207 . The performance of the algorithms was measured in terms of PAR reduction and energy cost minimization.…”

Section: Progress Of Dsm Optimization Models and Applications Of Algomentioning

confidence: 99%

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Progress on the demand side management in smart grid and optimization approaches

et al. 2020

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The integration of demand side management (DSM) with smart grid (SG) can facilitate residents' transfer into smart homes and sustainable cities by reducing the carbon emission. This manuscript reviews the recent works related to the application of DSM in SG through discussing the techniques and algorithms and their associated challenges for effective implementation. This paper also critically discusses the operation mode of DSM, the profile of energy production, storage and consumption, and finally the benefit obtained by the DSM implementation. Previous literature suggested that DSM practice reduced peak-to-average ratio, energy cost and carbon emission by approximately 10% to 65%, 5% to 50%, and 14%, respectively. The implementation of DSM in SG deals with a number of challenges such as security and privacy, tariff regulation, energy transmission, distribution, and effective utilization of energy resources. A number of international organizations have taken various measures and solutions to guarantee the security and privacy of the DSM in SG discussed. So far, a number of algorithms have been used as optimization approach to solve the DSM optimization problems; however hybrid algorithms have showed better performance than single algorithms due to their faster convergence speed. At the end, the paper presents the research gaps and future research directions.

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Section: Optimization Models With Emerging Techniques and Methodsmentioning

confidence: 99%

Section: Progress Of Dsm Optimization Models and Applications Of Algomentioning

confidence: 99%

Progress on the demand side management in smart grid and optimization approaches

et al. 2020

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“…Authors in Muralitharan et al, [5] and Zafar et al, [6] have implemented a Multi-Objective Evolutionary Algorithm (MOEA) and Harmony Search Algorithm (HSA), respectively. Both studies were trying to improve energy consumption and consumer comfort in the scope of a waiting period for appliances execution.…”

Section: Introductionmentioning

confidence: 99%

“…Both studies were trying to improve energy consumption and consumer comfort in the scope of a waiting period for appliances execution. However, they are only focused on consumer satisfaction in the context of the waiting period for the energy scheduling, but the indoor comfort index parameters are often ignored [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Methodology for Modified Whale Optimization Algorithm for Solving Appliances Scheduling Problem

Omar

Bakeri

Nawi

et al. 2020

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Whale Optimization Algorithm (WOA) is considered as one of the newest metaheuristic algorithms to be used for solving a type of NP-hard problems. WOA is known of having slow convergence and at the same time, the computation of the algorithm will also be increased exponentially with multiple objectives and huge request from n users. The current constraints surely limit for solving and optimizing the quality of Demand Side Management (DSM) case, such as the energy consumption of indoor comfort index parameters which consist of thermal comfort, air quality, humidity and vision comfort. To address these issues, this proposed work will firstly justify and validate the constraints related to the appliances scheduling problem, and later proposes a new model of the Cluster based Multi-Objective WOA with multiple restart strategy. In order to achieve the objectives, different initialization strategy and cluster-based approaches will be used for tuning the main parameter of WOA under different MapReduce application which helps to control exploration and exploitation, and the proposed model will be tested on a set of well-known test functions and finally, will be applied on a real case project i.e. appliances scheduling problem. It is anticipating that the approach can expedite the convergence of meta-heuristic technique with quality solution.

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“…6) Un Algoritmo Hibrido Genético-Taguchi fue desarrollado para gestionar la demanda de una residencia que incluye batería y generación fotovoltaica en Lin y Chen (2016) y es comparado con un algoritmo genético para demostrar su eficiencia. 7) En Zafar et al (2017) se propusieron tres algoritmos diferentes para la optimización de la demanda. Ellos son: Algoritmo de búsqueda harmónica, Algoritmo de optimización mediante forrajeo de bacterias y un algoritmo evolutivo mejorado, concluyendo que el primero es el más efectivo al reducir los costos.…”

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Gestión de la demanda en las redes inteligentes. Perspectiva y control desde el usuario y la distribuidora

et al. 2020

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El objetivo de este artículo es desarrollar el concepto de gestión de la demanda en relación a los tipos de control existentes y presentar un ejemplo simple de optimización de la demanda para evaluar las potencialidades de un control híbrido. Se presenta una breve introducción, detallando el conocimiento actual en gestión de demanda, se desarrolla el concepto de control directo e indirecto sobre la demanda, se identifica, y se elabora una tabla con las cargas residenciales. Se modelan dos residencias que podrán modificar su carga ante un esquema de precios dados y un objetivo de no concentración de la demanda fijados por la distribuidora, obteniendo una correcta optimización y dispersando las cargas de diferente forma en la segunda ronda. Se concluye que una intervención de la distribuidora puede mejorar la distribución de la demanda por la aleatoriedad del algoritmo genético sin comprometer la privacidad del usuario.Palabras clave: gestión de la demanda; respuesta a la demanda; redes inteligentes; optimización; algoritmo genético Demand management in smart networks.

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A Meta-Heuristic Home Energy Management System

Cited by 25 publications

References 19 publications

Progress on the demand side management in smart grid and optimization approaches

Progress on the demand side management in smart grid and optimization approaches

Methodology for Modified Whale Optimization Algorithm for Solving Appliances Scheduling Problem

Gestión de la demanda en las redes inteligentes. Perspectiva y control desde el usuario y la distribuidora

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