Smart home energy management, using IoT system

Hosseinian, Seyed Hossein; Damghani, Hamidreza

doi:10.1109/kbei.2019.8734979

Cited by 14 publications

(5 citation statements)

References 8 publications

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“…MQTT-based solutions have been used to measure humidity and temperature in rooms, with Raspberry Pi serving as a data access point [13]. Home automation using Arduino and Androidbased smartphones has also been proposed [32,33], although the feasibility of complex tasks and multitasking with Arduino is limited. Inexpensive sensors have been used for achieving home automation, with Android-based smartphones used as remote controls [34][35][36][37][38].…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Home Automation System with Emergency Control

Mishra,

Gupta,

Batra

et al. 2024

J. Sci. Res.

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In today's society, there is a growing demand for smart home solutions that not only enhance convenience but also make a significant impact on affordability and widespread adoption. This paper describes the proposed design of a Node MCU-based Home Automation system with an integrated Wi-Fi module that enables internet-based remote control of home devices. The design utilizes a basic code template to connect with the Tasmota Web interface to configure the ESP module. The system is integrated with Alexa, offering various integration options such as routines, schedules and alarms. Furthermore, it adds a manual control feature for situations where there is no internet connection and an additional level of safety through emergency controls in the event of electronic failure. A comparison of energy usage with and without the use of the proposed system is also reported. A noticeable decrease in daily electricity consumption is observed on using the designed automation system. To ensure cost-effectiveness, locally available components are utilized in the design. Consequently, it ensures that enhanced comfort, convenience, and control over one's living environment are available to a wider audience.

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Section: Literature Reviewmentioning

confidence: 99%

Design of Home Automation System with Emergency Control

Mishra,

Gupta,

Batra

et al. 2024

J. Sci. Res.

View full text Add to dashboard Cite

show abstract

“…where 𝑏 𝑗 1 characterises the bias for the j th feature map, where w is the weight of the kernel, m is the catalogue value of the filter, and σ is the initiation feature similar to ReLU. The vector 𝑦 𝑖𝑗 1 generated by the lth convolutional layer is the result in equation (2).…”

Section: B Cnn-lstm Networkmentioning

confidence: 99%

“…The emergence of IoT has simplified the design of smart homes which reduces the carbon footprint and improve the sustainability of energy management systems (EMS). In this context, there is a great demand for smart energy management techniques for smart homes [2]. IoT technologies enable the conventional EMS to sense the data collected across different sensors and helps in the monitoring and controlling of energy consumption by household appliances.…”

Section: Introductionmentioning

confidence: 99%

Grey Wolf Optimization-Based CNN-LSTM Network for the Prediction of Energy Consumption in Smart Home Environment

Singh,

Solanki,

Sharma

et al. 2023

IEEE Access

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In smart homes, the management of energy is gaining huge significance among researchers in recent times. This paper presents a system for predicting power utilization and scheduling household appliances in smart homes. The system utilizes a combination of Grey Wolf optimization (GWO), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) to improve energy management. The GWO algorithm is used to enhance the performance of the CNN-LSTM model. GWO is an optimization algorithm inspired by the hunting behaviour of grey wolves. It helps in finding optimal solutions for complex problems by mimicking the social hierarchy and hunting mechanisms of wolves. The fusion of CNN and LSTM serves as a pattern finding strategy for energy management. CNN is effective in extracting spatial features from data, while LSTM can capture temporal dependencies. By combining these two approaches, the model can analyze energy consumption patterns and make accurate predictions. To evaluate the performance of the proposed model, the paper uses three error metrics: Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). The reported values of RMSE, MSE, and MAE are 0.6213, 0.3860, and 0.2808, respectively. These metrics indicate the accuracy of the model's predictions, with lower values indicating better performance. Furthermore, this paper compares the proposed approach with the existing baseline models to access its superiority. According to the results, the proposed model outperforms the existing approaches in terms of prediction accuracy, as it achieves lower errors, compared to the baseline models. In summary, the proposed GWO-based CNN-LSTM network demonstrates improved prediction accuracy compared to the existing approaches, as indicated by the evolution metrics.

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“…In [21] was presented a discussion of social impacts on reduced energy consumption in homes. The review presented in [203] is discussing energy management propositions over the literature. In [102] was presented research on the impacts of smart home energy management on the development in other disciplines.…”

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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Smart home energy management, using IoT system

Cited by 14 publications

References 8 publications

Design of Home Automation System with Emergency Control

Design of Home Automation System with Emergency Control

Grey Wolf Optimization-Based CNN-LSTM Network for the Prediction of Energy Consumption in Smart Home Environment

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

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