Optimal scheduling model for smart home energy management system based on the fusion algorithm of harmony search algorithm and particle swarm optimization algorithm

Zhang, Zhisheng; Wang, Jidong; Zhong, Haitao; Ma, Hanjie

doi:10.1080/23744731.2019.1690922

Cited by 25 publications

(17 citation statements)

References 12 publications

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“…The predictive approach uses automatic systems to control appliances energy consumption based on real-time data collected by sensors known as dynamic approach or appliance energy consumption profile data such as occupancy schedules, fixed setpoint, etc. On the other hand, the non-predictive approach is mostly remote controlled via mobile applications and web services through a central control system such as a smart meter [8,[31][32][33]. In this context, this study classified selected literature into predictive and non-predictive approaches, as presented in Figure 6.…”

Section: Literature Classification Based On Control Approachmentioning

confidence: 99%

Systematic Review Analysis on Smart Building: Challenges and Opportunities

et al. 2022

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Smart building technology incorporates efficient and automated controls and applications that use smart energy products, networked sensors, and data analytics software to monitor environmental data and occupants’ energy consumption habits to improve buildings’ operation and energy performance. Smart technologies and controls are becoming increasingly important not only in research and development (R&D) but also in industrial and commercial domains, leading to a steady growth in their application in the building sector. This study examines the literature on SBEMS published between 2010 and 2020 with a systematic approach. It examines the trend with the annual number of the published studies before exploring the classification of publications in terms of factors such as domain of SBEMS, control approaches, smart technologies, and quality attributes. Recent developments around the smart building energy management systems (SBEMS) have focused on features that provide occupants with an interface to monitor, schedule, and modify building energy consumption profiles and allow a utility to participate in a communication grid through demand response programs and automatic self-report outage functionality. The study also explores future research avenues, especially in terms of improvements in privacy and security, and interoperability. It is also suggested that the smart building technologies’ smartness can be improved with the help of solutions such as real-time data monitoring and machine learning

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Section: Literature Classification Based On Control Approachmentioning

confidence: 99%

Systematic Review Analysis on Smart Building: Challenges and Opportunities

et al. 2022

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“…A model of classic particle swarm optimization for battery usage was proposed in [212]. The fusion of harmony search algorithm and particle swarm optimization was proposed in [213]. The study discusses how to compose an energy management model for a smart home system, where to hybrid algorithm optimizes scheduling for environmental change, costs of energy, user habits, and various loads of the infrastructure in different periods of the year.…”

Section: Optimal Energy Management and Sustainabilitymentioning

confidence: 99%

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

et al. 2021

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The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this paper, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry. INDEX TERMS Internet of Things (IoT), smart home, healthcare, energy management, security. I. INTRODUCTION O VER time, the type of home was evolving. The shape of the house was evolving during ages, cultural revolutions, and climate changes. We have divided our homes into rooms devoted to sleeping, cooking, work, children, sanitary needs, warehousing, etc. People started to settle in various locations and continents.

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“…GA was used to solve the multiobjective optimization problem. A day-ahead load forecasting was assumed before scheduling, and a hybrid Harmony Search-PSO algorithm was used for optimal scheduling via a human-machine interface, central controller, and different loads [125].…”

Section: Ec Based Dsmmentioning

confidence: 99%

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

et al. 2021

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The ever increasing demand for electricity and the rapid increase in the number of automatic electrical appliances have posed a critical energy management challenge for both utilities and consumers. Substantial work has been reported on the Home Energy Management System (HEMS) but to the best of our knowledge, there is no single review highlighting all recent and past developments on Demand Side Management (DSM) and HEMS altogether. The purpose of each study is to raise user comfort, load scheduling, energy minimization, or economic dispatch problem. Researchers have proposed different soft computing and optimization techniques to address the challenge, but still it seems to be a pressing issue. This paper presents a comprehensive review of research on DSM strategies to identify the challenging perspectives for future study. We have described DSM strategies, their deployment and communication technologies. The application of soft computing techniques such as Fuzzy Logic (FL), Artificial Neural Network (ANN), and Evolutionary Computation (EC) is discussed to deal with energy consumption minimization and scheduling problems. Different optimization-based DSM approaches are also reviewed. We have also reviewed the practical aspects of DSM implementation for smart energy management.

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Optimal scheduling model for smart home energy management system based on the fusion algorithm of harmony search algorithm and particle swarm optimization algorithm

Cited by 25 publications

References 12 publications

Systematic Review Analysis on Smart Building: Challenges and Opportunities

Systematic Review Analysis on Smart Building: Challenges and Opportunities

Smart Homes: How Much Will They Support Us? A Research on Recent Trends and Advances

A Comprehensive Review on Residential Demand Side Management Strategies in Smart Grid Environment

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