Optimization Techniques for Home Energy Management: A Review

Qayyum, Nabeeha; Amin, Adil; Jamil, Umar; Mahmood, Anzar

doi:10.1109/icomet.2019.8673435

Cited by 20 publications

(8 citation statements)

References 17 publications

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“…However, there were some attempts that were based on other evolutionary or collective behavior of other living organisms. Examples of such algorithms include the use of shuffled frog leaping optimization, graywolf optimization [146] for power management and also for traffic routing [147], earthworm optimization [148] for power management in smart grid, vehicle routing using simulated annealing [149] and several different ones for home energy management [150] and elephant herding optimization [151] as well in addition to others.…”

Section: Novel Nature Inspired/heuristic Algorithmsmentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

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One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

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Section: Novel Nature Inspired/heuristic Algorithmsmentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…IENs are used to illustrate a broad idea, including intelligent power grids, smart district heating (DH) networks, and intelligent natural gas systems. In recent years, IENs have evolved exceptionally quickly to satisfy the growing need for energy in a strong, flexible, environment-friendly, and cost-efficient way [6]. The essential components of IENs are smart energy meters, which are used to operate household machines.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

AlZaabi¹,

Shaalan²,

Ghazal³

et al. 2023

Computers, Materials &Amp; Continua

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Energy is essential to practically all exercises and is imperative for the development of personal satisfaction. So, valuable energy has been in great demand for many years, especially for using smart homes and structures, as individuals quickly improve their way of life depending on current innovations. However, there is a shortage of energy, as the energy required is higher than that produced. Many new plans are being designed to meet the consumer's energy requirements. In many regions, energy utilization in the housing area is 30%-40%. The growth of smart homes has raised the requirement for intelligence in applications such as asset management, energy-efficient automation, security, and healthcare monitoring to learn about residents' actions and forecast their future demands. To overcome the challenges of energy consumption optimization, in this study, we apply an energy management technique. Data fusion has recently attracted much energy efficiency in buildings, where numerous types of information are processed. The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate. The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%, which is higher than the previously published approaches.

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“…The objective of these systems is to find the optimal consumption schedule for consumers considering various factors such as their consumption profiles, energy cost and environmental concerns. Comprehensive literature reviews have been conducted in this area where factors such as DR programmes, optimization techniques and smart technologies have been considered [16][17][18][19][20][21][22][23]. In a more advanced approach, Antonopoulos et al [24] have provided a comprehensive systematic review on applying Artificial Intelligence (AI) and Machine Learning (ML) techniques to Home Energy Management Systems (HEMS) and network optimization by predicting the available DR capacity and price adjustment.…”

Section: Introductionmentioning

confidence: 99%

Residential Demand Response Strategies and Applications in Active Distribution Network Management

Davarzani

Pisică

Munisami

2021

Renewable and Sustainable Energy Reviews

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Optimization Techniques for Home Energy Management: A Review

Cited by 20 publications

References 17 publications

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Residential Demand Response Strategies and Applications in Active Distribution Network Management

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