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, 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.

Futuristic Research Perspectives of IoT Platforms

Future IoT innovation patterns will assist offices with getting the greatest proficiency and efficiency out of their hardware and assembling parts. IoT is an essential element of digital transformation enterprises in business and industrial sections. Service suppliers and utilities have also been taking on IoT to get pioneering services to keep competitive. Services with security, power management, asset presentation, healthcare effectiveness, and threat and agreement management must be resolved properly in order to enhance the IoT effectively and efficiently. As new tech turns up, hackers prepare to capture the benefits of its potential flaws, and this is precisely why enhancing the precautions of associated strategy is the top IoT technology development. Objectives of this chapter are to analyze and access the future of IoT in healthcare, security, education, and agriculture. This chapter will focus on edge computing, a hybrid approach to process the data that allows connected devices to distribute, compute, examine, and maintain data locally.