HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Machorro-Cano, Isaac; Alor-Hernández, Giner; Paredes-Valverde, Mario Andrés; Rodríguez-Mazahua, Lisbeth; Sánchez-Cervantes, José Luis; Olmedo-Aguirre, José Oscar

doi:10.3390/en13051097

Cited by 119 publications

(69 citation statements)

References 81 publications

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“…A machine learning-based smart home energy system was investigated in ( Machorro-Cano et al., 2020 ), using big data with the support of IoT. The home automatization system was coupled with IoT devices that enabled energy savings for the given purpose.…”

Section: Iot Technologies In Sustainable Energy and Environmentmentioning

confidence: 99%

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

Nižetić

Šolić

López-de-Ipiña

et al. 2020

Journal of Cleaner Production

640

165

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The rapid development and implementation of smart and IoT (Internet of Things) based technologies have allowed for various possibilities in technological advancements for different aspects of life. The main goal of IoT technologies is to simplify processes in different fields, to ensure a better efficiency of systems (technologies or specific processes) and finally to improve life quality. Sustainability has become a key issue for population where the dynamic development of IoT technologies is bringing different useful benefits, but this fast development must be carefully monitored and evaluated from an environmental point of view to limit the presence of harmful impacts and ensure the smart utilization of limited global resources. Significant research efforts are needed in the previous sense to carefully investigate the pros and cons of IoT technologies. This review editorial is partially directed on the research contributions presented at the 4th International Conference on Smart and Sustainable Technologies held in Split and Bol, Croatia, in 2019 (SpliTech 2019) as well as on recent findings from literature. The SpliTech2019 conference was a valuable event that successfully linked different engineering professions, industrial experts and finally researchers from academia. The focus of the conference was directed towards key conference tracks such as Smart City, Energy/Environment, e-Health and Engineering Modelling. The research presented and discussed at the SpliTech2019 conference helped to understand the complex and intertwined effects of IoT technologies on societies and their potential effects on sustainability in general. Various application areas of IoT technologies were discussed as well as the progress made. Four main topical areas were discussed in the herein editorial, i.e. latest advancements in the further fields: (i) IoT technologies in Sustainable Energy and Environment, (ii) IoT enabled Smart City, (iii) E-health – Ambient assisted living systems (iv) IoT technologies in Transportation and Low Carbon Products. The main outcomes of the review introductory article contributed to the better understanding of current technological progress in IoT application areas as well as the environmental implications linked with the increased application of IoT products.

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Section: Iot Technologies In Sustainable Energy and Environmentmentioning

confidence: 99%

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

Nižetić

Šolić

López-de-Ipiña

et al. 2020

Journal of Cleaner Production

640

165

View full text Add to dashboard Cite

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“…There is no doubt that deep learning was an excellent strategy for dealing with a nonlinear issue of stationary human recognition. The IoT platform with a machine learning-based energy monitoring system is a promising approach for monitoring smart buildings to reduce energy consumption and simultaneously remain comfort and safety [ 18 ]. In ref.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Elsisi

Tran

Mahmoud

et al. 2021

Sensors

137

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Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices.

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“…This dream can be fulfilled only with IoT based smart home. Artificial intelligence and machine learning are becoming more and more popular in IoT-based smart homes [7].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Real-Time Implementation of IoT in Autism

Tayyaba

et al. 2021

Preprint

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The present study comprises the importance and use of IoT, smart home, peer-to-peer, and some other different technologies for the betterment of mankind. Artificial intelligence and machine learning are becoming more and more popular in IoT-based smart homes and other techs. The IoT framework has been used to provide a predictable, concrete, and self-paced learning environment and encourages excellent visual information processing. These techs are also useful for special children with autism. They need smart homes, peer-to-peer networking, robot therapy, and wearable techs. In this paper, the authors presented a unique FLC simulation for the behavior of a child with moderate autism. This simulation shows that how IoT, smart home, peer-to-peer technology, ABA therapy, robot therapy, and wearable techs can bring comforts in the life of a child with autism. All of them have long lasted impacts in producing social skills in an ASD person. The 3D graphical figures presented the graphical analysis of a child’s social behavior. The simulation presented 50% betterment change in the social behavior of children and this can be increased up to 87% by using intensive therapies. It is also verified by Mamdani’s method. A real-time implementation of a boy with autism has shown significant improvements in his social skills.

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HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Cited by 119 publications

References 81 publications

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

Analysis and Real-Time Implementation of IoT in Autism

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