Transformation of Smart Grid using Machine Learning

Azad, Salahuddin; Sabrina, Fariza; Wasimi, Saleh A.

doi:10.1109/aupec48547.2019.211809

“…But in reallife applications, power wastage is occurring in monitoring links also. Salahuddin Azad et al (Azad et al, 2019) discussed the ML based transformation in SG. The ML algorithms were used for intelligent decision-making and response to customer requests, unexpected changes in power distribution, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Random forest based power sustainability and cost optimization in smart grid

Durairaj¹,

Wróblewski

²

,

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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“…Adequacy analysis is carried out by looking at consumer flexibility and performance analysis of energy management systems can be carried out using the MATLAB/Simulink platform [6]. Beside the MATLAB program, the analysis of energy system can be used hybrid optimization of multiple energy resources (HOMER) software [13] and machine learning [14]- [16].…”

Section: Issn: 2088-8694mentioning

confidence: 99%

Appropriateness analysis of implementing a smart grid system in campus buildings using the fuzzy method

Maizana

¹

,

Putri

²

2022

IJPEDS

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The smart building campus is still being studied by researchers until now. Given the importance of meeting key performance indicators on smart campuses. Medan Area University is a campus that uses a smart grid system to solve energy management problems. With the position of the Medan Area University being in an area that has the potential to utilize photovoltaic (PV) solar renewable energy sources. With the designation of Google Earth data, the average sunlight intensity in the campus area is 4,528 KWh/m2. The design of the smart grid system is carried out by making solar PV solar as the main source and 4 computers as loads. The fuzzy method was chosen in the smart grid system simulation that has been designed because the fuzzy method can provide decisions in the intersection area. To facilitate analysis The MATLAB program is used to assist in the simulation. So that the output can show results that are close to real conditions. Based on 144 hours of data that has been entered into the fuzzy method, the output shows the results are appropriate to be implemented.

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“…But in real-life applications, power wastage is occurring in monitoring links also. Salahuddin Azad et al [28] discussed the ML based transformation in SG. The ML algorithms were used for intelligent decision-making and response to customer requests, unexpected changes in power distribution, etc.…”

Section: Related Workmentioning

confidence: 99%

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

Durairaj¹,

Wróblewski²,

Sheela³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

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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Transformation of Smart Grid using Machine Learning

Cited by 35 publications

References 26 publications

Random forest based power sustainability and cost optimization in smart grid

Random forest based power sustainability and cost optimization in smart grid

Appropriateness analysis of implementing a smart grid system in campus buildings using the fuzzy method

Random Forest Based Power Sustainability and Cost Optimization in Smart Grid

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