<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</abstract>

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.

Random forest based power sustainability and cost optimization in smart grid

Production Engineering Archives

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

{"article-title":"Random forest based power sustainability and cost optimization in smart grid"}

Presently power control and management play a vigorous role in information technology and power management. Instead of non-renewable power manufacturing,...

Random forest based power sustainability and cost optimization in smart grid

Pubblicato online: 12 feb 2022

Pagine: 82 - 92

Ricevuto: 10 dic 2021

Accettato: 17 gen 2022

DOI: https://doi.org/10.30657/pea.2022.28.10

Parole chiave
Smart Grid, Random Forest, Internet of Things, Power management, Machine Learning, Smart Meter, Priority Power Scheduling

© 2022 Danalakshmi Durairaj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Random forest based power sustainability and cost optimization in smart grid

Pubblicato online: 12 feb 2022

Pagine: 82 - 92

Ricevuto: 10 dic 2021

Accettato: 17 gen 2022

DOI: https://doi.org/10.30657/pea.2022.28.10

Parole chiaveSmart Grid, Random Forest, Internet of Things, Power management, Machine Learning, Smart Meter, Priority Power Scheduling

© 2022 Danalakshmi Durairaj et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Parole chiave
Smart Grid, Random Forest, Internet of Things, Power management, Machine Learning, Smart Meter, Priority Power Scheduling