Study on Smart Home Energy Management System Based on Artificial Intelligence

Ma, Yunlong; Chen, Xiao; Wang, Liming; Yang, Jianlan

doi:10.1155/2021/9101453

Cited by 18 publications

(8 citation statements)

References 26 publications

(23 reference statements)

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“…e vehicle is tested with two different energy cases, low and high, where a learning model uses previous data set for identification cases thus a line of energy intersection is present. Further, a home management system using AI procedures is incorporated with a specialized metering scheme that is operated under automated mode [22]. But if the metering scheme is introduced then a local management terminal must be designed for transmitting the information to different consumers.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Even the above-mentioned case studies are analyzed by several researchers in Morocco where energyefficient operation is achieved only if AI is incorporated. All the existing models [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] focuses only on different renewable energy sources to forecast various behavior of appliances in real-time environmental conditions. But most of the procedures that are present in existing methodologies are not introduced with high-end monitoring devices and even the analytical framework is not framed.…”

Section: Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Prophetic Energy Assessment with Smart Implements in Hydroelectricity Entities Using Artificial Intelligence Algorithm

Alqahtani

Kshirsagar

Manoharan³

et al. 2022

International Transactions on Electrical Energy Systems

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An encouraging development is the quick expansion of renewable energy extraction. Harnessing renewable energy is economically feasible at the current rate of technological advancement. Traditional energy sources, such as coal, petroleum, and hydrocarbons, which have negative effects on the environment, are coming under more social and financial pressure. Companies need more solar and wind power because this calls for a well-balanced mix of renewable resources and a higher proportion of alternative energy sources. Sustainable energy can be captured using a variety of techniques. Massive scale and small-sized are the two most prevalent techniques. No renewable energy source possesses an inherent property that restricts how it may be managed or how it can be planned to produce electricity. A number of factors have contributed to a growth in the use of alternative sources, one of which is to mitigate the effects of rising temperatures. To improve the ability to estimate renewable energy, various modeling approaches have been created. This region might use an HRES to give many sources with the inclusion of different energy sources. The inventiveness of solar and wind power and the brilliant ability of neural networks to handle complex time-series data signals have both aided in the prediction of sustainable energy. Therefore, this research will examine the numerous information models in order to determine which proposed models can provide accurate projections of renewable energy output, such as sunlight, wind, or pumped storage. In the fields of sustainable energy predictions, a number of machine learning methods, such as multilayer perceptions MLP, RNN CNN, and LSTM designs, are frequently utilized. This form of modeling uses historical data to predict potential values and can predict short-term patterns in solar and wind generation.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Prophetic Energy Assessment with Smart Implements in Hydroelectricity Entities Using Artificial Intelligence Algorithm

Alqahtani

Kshirsagar

Manoharan³

et al. 2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

show abstract

“…It also explores the future prospects of MAIoT in the context of smart homes. By the end of this paper, readers will gain a comprehensive understanding of how MAIoT can be leveraged to create more energyefficient and user-friendly smart homes, offering benefits to both homeowners and the environment [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Intelligent-of-things multiagent system for smart home energy monitoring

Vemuri,

Vijaya Durga,

Syed Ibrahim

et al. 2024

IJEECS

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The proliferation of IoT devices has ushered in a new era of smart homes, where efficient energy management is a paramount concern. Multiagent artificial intelligence-of-things (MAIoT) has emerged as a promising approach to address the complex challenges of smart home energy management. This research study examines MAIoT's components, functioning, benefits, and drawbacks. MAIoT systems improve energy efficiency and user comfort by combining multiagent systems and IoT devices. However, privacy, security, interoperability, scalability, and user acceptability must be addressed. As technology advances, MAIoT in smart home energy management will offer more sophisticated and adaptable solutions to cut energy consumption and promote sustainability. This article describes how energy status and internal pricing signals affect group intelligent decision making and the interaction dynamics between consumers or decision makers. In a multiagent configuration based on the new concept of artificial intelligence-of-things, this intelligent home energy management challenge is simulated and illustrated using software and hardware. Based on sufficient experimental simulations, this paper suggested that residential clients can significantly improve their economic benefit and decision-making efficiency.

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“…ree different scenarios have been considered to solve the energy management problem in smart homes with the following three objective functions: e optimal time of indoor appliances by buffering the storage device is also presented in [38] to minimize costs as the objective of the optimization problem. Similarly, a household appliance participation algorithm for household load scheduling was introduced in [39] to reduce the cost of electricity consumption. Beyond what has been stated in the demand-side management of smart networks, several methods in recent work have been used for household energy management and task scheduling.…”

Section: Introductionmentioning

confidence: 99%

A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

Dashtaki

Khaki

Zand

et al. 2022

International Transactions on Electrical Energy Systems

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The concept of energy management in smart homes has received increasing attention in recent years, particularly on issues such as creating a balance between user privacy and reducing energy costs. Accordingly, this article proposes a user-oriented multi-objective approach, which minimizes energy costs and maximizes consumer privacy. In addition, a home energy management system is suggested for smart homes to optimize the energy consumption pattern of appliances. On the other hand, considering challenges in energy management of smart homes, the concept of demand-side management (DSM) is introduced. The objective of the proposed method is to reduce energy consumption to lower consumers’ electricity bills. Also, it improves user comfort (UC) in average waiting time conditions. In this research, a smart home equipped with an energy management system and smart home appliances that can inject electric power into the upstream network is considered the main system. This framework leads to a multi-objective optimization problem in which the two objectives mentioned above are considered two separate dimensions. To solve the problem, an ITS-BF Algorithm is used, which employs a random search to schedule home appliances and batteries based on the application of flexible devices in smart homes. The case studies show that the proposed method can considerably respect and satisfy users’ privacy and reduce the energy cost to an acceptable level. Finally, the numerical results obtained from the simulation have been analyzed to evaluate the proposed method’s efficiency. The simulation results show that an ITS-BF algorithm performs better than the existing methods in reducing costs and waiting time.

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Study on Smart Home Energy Management System Based on Artificial Intelligence

Cited by 18 publications

References 26 publications

Prophetic Energy Assessment with Smart Implements in Hydroelectricity Entities Using Artificial Intelligence Algorithm

Prophetic Energy Assessment with Smart Implements in Hydroelectricity Entities Using Artificial Intelligence Algorithm

Intelligent-of-things multiagent system for smart home energy monitoring

A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm

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