A comprehensive analysis of smart home energy management system optimization techniques

Molla, Tesfahun; Khan, B. Zorina; Singh, Pawan

doi:10.32629/jai.v1i1.14

Cited by 29 publications

(23 citation statements)

References 32 publications

(28 reference statements)

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“…While applying these new technologies, the test for retrieving information from crops is to come out with [20]. [9], [23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

Saqib¹,

Ahmad²

2021

J. Infor. Electr. Electron. Eng.

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The Internet of Things (IoT) has achieved an upset in a considerable lot of the circles of our current lives, like automobile, medical services offices, home automation, retail, education, manufacturing, and many more. The Agriculture and Farming ventures significantly affect the acquaintance of the IoT with the world. Machine learning (ML) is a part of artificial intelligence (AI) that permits software applications to turn out to be more precise at foreseeing results without being expressly customized to do as such. It uses historical data as input to predict new result values. In the event, a specific industry has sufficient recorded information to help the machine "learn", AI or ML can create outstanding outcomes. Farming is likewise one such important industry profiting and advancing from machine learning at large. ML can possibly add to the total lifecycle of farming, at all phases. This incorporates computer vision, automated irrigation, and harvesting, predicting the soil, weather, temperature, moisture values, and robots for picking off the crude harvest. In this paper, I'll work on a smart agricultural information monitoring framework that gathers the necessary information from the IoT sensors set in the field, measures it, and drives it, from where it streams to store in the cloud space. The information is then shipped off the prediction module where the necessary analysis is done using ML algorithms and afterward sent to the UI for its corresponding application.

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“…While applying these new technologies, the test for retrieving information from crops is to come out with [20]. [9], [23][24][25][26][27][28][29][30][31][32][33][34].…”

Section: Literature Reviewmentioning

confidence: 99%

An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

Saqib¹,

Ahmad²

2021

J. Infor. Electr. Electron. Eng.

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“…Only 10 papers focus on EMS in non-residential buildings, such as hotels or office buildings. 4,38,59,62,68,72,84,91,97,98 Photovoltaic systems and battery storage are also frequently considered, with 64% and 56% respectively. The sector coupling of electricity and heat by means of heat pumps or CHP units is less relevant and is dealt with in only 13% and 5% of the literature.…”

Section: Considered Componentsmentioning

confidence: 99%

“…The high share of household appliances in the design of HEMS is based on the fact that 88 out of 98 papers focus on EMS of residential buildings and households. Only 10 papers focus on EMS in non‐residential buildings, such as hotels or office buildings 4,38,59,62,68,72,84,91,97,98 . Photovoltaic systems and battery storage are also frequently considered, with 64% and 56% respectively.…”

Section: Quantitative Evaluation Of the Literature Reviewmentioning

confidence: 99%

Overview of the current state of research on characteristics and algorithms of energy management systems in households and buildings

Schminke

2021

Intl J of Energy Research

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The decentralization of energy supply and the fluctuating feed-in from renewable energies lead to an increasing need to generate electricity and heat locally and efficiently. Therefore, different energy systems are used together at building level. Consequently, energy management systems (EMSs) in the building sector and households are becoming increasingly complex and are facing new challenges. To face these challenges, EMSs are required to enable efficient and combined operation of multiple energy systems and components. The increasing research into EMS is leading to a growing field of research with different characteristics, objectives, and algorithms. However, the resulting diversification of the research topic is accompanied by a lack of transparency. Therefore, this article is the first to provide a comprehensive quantitative evaluation of the state of research. For this purpose, 98 relevant publications are quantitatively evaluated. This article focuses on methods, model characteristics, optimization objectives, price structures, observation horizon, and components and devices used of EMS. The detailed evaluation of these topics creates a high level of transparency in EMS research to provide precise insights into current research priorities and potential weaknesses, to allow researchers to classify their own work and to identify potential research topics. Highlights• For the first time, a quantitative study on EMS research is presented • The evaluation of 98 relevant publications provides detailed insights • Insights about methods, characteristics, objectives, prices, horizons, and components • Detailed graphical evaluations for each sub-area of the study K E Y W O R D S energy price structures, energy management systems in buildings, optimization goals, overview

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“…The use of blockchain is another tool that can be used to improve privacy when exchanging healthcare data. With the support of this system, since they are extracted from the blockchain network, healthcare organizations will not have to worry about the reliability of the Journal of Informatics Electrical and Electronics Engineering (JIEEE) A2Z Journals results, and therefore the consumers themselves will be able to handle them [54][55][56][57][58][59][60][61][62]. The multimedia data (e.g., images, sounds, and videos) is one form of healthcare data.…”

Section: Privacy and Security In The Healthcare Blockchainmentioning

confidence: 99%

The Impact of Blockchain on the Healthcare Environment

Praveen¹

2021

J. Infor. Electr. Electron. Eng.

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Bitcoin was the first electronic payment system to truly exploit the power of blockchain technology. There is currently the problem of health information inequality and health information leakage. Physicians should conduct essential routine work that wastes human and financial resources and delays treatment processes. Blockchain provides a trust-free and cost-reducing solution to manage and secure valuable health information. The aim of this study is to discuss research into blockchain healthcare applications. It addresses the management of medical data, as well as the sharing of medical information, the sharing of images, and the management of logs. We also discuss papers that overlap with other fields, such as the Internet of Things, information management, drug monitoring along their supply chain, and aspects of security and privacy. Finally, we analyze and compare the research papers in the medical area and also summarize the strategies used in healthcare with their pros and cons.

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A comprehensive analysis of smart home energy management system optimization techniques

Cited by 29 publications

References 32 publications

An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

Overview of the current state of research on characteristics and algorithms of energy management systems in households and buildings

The Impact of Blockchain on the Healthcare Environment

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