A smart grid incorporated with ML and IoT for a secure management system

Dharmadhikari, S. C.; Gampala, Veerraju; Rao, Ch. Mallikarjuna; Khasim, Syed; Jain, Shafali; Bhaskaran, R.

doi:10.1016/j.micpro.2021.103954

Cited by 33 publications

(12 citation statements)

References 19 publications

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“…A secure demand-side management (DSM) engine was proposed using machine learning (ML) for the Internet of Things (IoT)-enabled grid. 25 An open-source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet. 26 Patel et al 27 developed a scaled-down prototype of an IoT-enabled datalogger for photovoltaic system that is installed in a remote location where human intervention was not possible due to harsh weather conditions or other circumstances.…”

Section: Related Workmentioning

confidence: 99%

“…A secure demand‐side management (DSM) engine was proposed using machine learning (ML) for the Internet of Things (IoT)‐enabled grid 25 . An open‐source technology based smart system to predict the irrigation requirements of a field using the sensing of ground parameter like soil moisture, soil temperature, and environmental conditions along with the weather forecast data from the Internet 26 …”

Section: Related Workmentioning

confidence: 99%

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IOT‐HML: A hybrid machine learning technique for IoT enabled industrial monitoring and control system

Kota

Chandak²,

Patil

2022

Concurrency and Computation

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Industry 4.0 makes manufacturers more vulnerable to current challenges and makes it easier to adapt to market changes. This will increase the speed of innovation, make it more customer-oriented and lead to faster design processes. It is essential to focus on monitoring and controlling the production system before complex accidents occur. Moreover, an industrial control system facing information security problems in recent times because of the nature of IoT which affects the evaluation of abnormal predication. To overcome above research gaps, we shift to industrial 4.0 which combine IoT and mechanism learning for industrial monitor and manage. We propose a hybrid machine learning technique for IoT enabled industrial monitoring and control system (IoT-HML). Here, we concentrate both information security issues with accurate monitoring and control system. The first section of proposed IoT-HML system is to introduce the cat induced wheel optimization (IWO) algorithm for cluster formation. The process consists of clustering and cluster head (CH) selection. The source node forward information to destination through CH only which avoids the unwanted data loss and improve the security, because the information travel through trusted path. For route selection process, we utilize the cuckoo search algorithm to compute the optimal best path among multiples. In second section, we illustrate a coach and player learned neural network (CP-LNN) for monitoring the industrial and prevent from accidents by basic control strategies. Finally, the proposed IoT-HML system can evaluate with different set of data's to prove the effectiveness.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

IOT‐HML: A hybrid machine learning technique for IoT enabled industrial monitoring and control system

Kota

Chandak²,

Patil

2022

Concurrency and Computation

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“…The IoT devices collect power from various sources and are stored in SG. The collected power distributed the consumer with the help of the ML classification algorithms (Dharmadhikari et al, 2021). The ML algorithms are also used to schedule the power between smart cities.…”

Section: Introductionmentioning

confidence: 99%

Random forest based power sustainability and cost optimization in smart grid

Durairaj¹,

Wróblewski

Joseph³

et al. 2022

Production Engineering Archives

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Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing, renewable power manufacturing is preferred by every organization for controlling resource consumption, price reduction and efficient power management. Smart grid efficiently satisfies these requirements with the integration of machine learning algorithms. Machine learning algorithms are used in a smart grid for power requirement prediction, power distribution, failure identification etc. The proposed Random Forest-based smart grid system classifies the power grid into different zones like high and low power utilization. The power zones are divided into number of sub-zones and map to random forest branches. The sub-zone and branch mapping process used to identify the quantity of power utilized and the non-utilized in a zone. The non-utilized power quantity and location of power availabilities are identified and distributed the required quantity of power to the requester in a minimal response time and price. The priority power scheduling algorithm collect request from consumer and send the request to producer based on priority. The producer analysed the requester existing power utilization quantity and availability of power for scheduling the power distribution to the requester based on priority. The proposed Random Forest based sustainability and price optimization technique in smart grid experimental results are compared to existing machine learning techniques like SVM, KNN and NB. The proposed random forest-based identification technique identifies the exact location of the power availability, which takes minimal processing time and quick responses to the requestor. Additionally, the smart meter based smart grid technique identifies the faults in short time duration than the conventional energy management technique is also proven in the experimental results.

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“…The rapid developments in internet of things (IoT) technologies have contributed to new research interests in the field of energy management [8]- [11]. In the residential sector, the energy management system  ISSN: 2302-9285 plays a vital role in managing demand for achieving better energy efficiency through smart control of various home entities.…”

Section: Introductionmentioning

confidence: 99%

Embedded control unit design for energy management in smart homes

Abusharia¹,

Al-Aubidy²

2022

Bulletin EEI

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This paper deals with smart home energy management through load scheduling and optimal use of available energy sources. In this study, three energy sources were considered: the national electricity grid, photovoltaic (PV) energy, and the storage unit. The PV array can provide the maximum power to the load at a given operating point where the output power changes with temperature, radiation and load. Therefore, a real-time controller is proposed to track the maximum power. An energy management algorithm has been proposed in a smart home to achieve the main goal of making the electricity bill as low as possible. The algorithm involves scheduling loads by assigning a priority to each load. The loads are supplied with the required power according to their priorities and the available energy. The obtained results indicate that supplying the PV system with a fuzzy-based MPPT indicates an increase in system efficiency. The results also showed that the use of energy management based on load scheduling led to a significant reduction in the electricity bill.

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A smart grid incorporated with ML and IoT for a secure management system

Cited by 33 publications

References 19 publications

IOT‐HML: A hybrid machine learning technique for IoT enabled industrial monitoring and control system

IOT‐HML: A hybrid machine learning technique for IoT enabled industrial monitoring and control system

Random forest based power sustainability and cost optimization in smart grid

Embedded control unit design for energy management in smart homes

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