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Abstract
The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, transmission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches.
Highlights
The application of a keen meter for controlling and overseeing electric force utilization is one of the advances that help both clients just as electric forces and providers
A deep extreme machine learning approach applied to feature-based data provided a better result
The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches
Summary
The application of a keen meter for controlling and overseeing electric force utilization is one of the advances that help both clients just as electric forces and providers. IoT is the interconnection of different incorporated processing gadgets on the Internet, which allow them to speak with one another This improves the personal satisfaction of the end client. AMI is unquestionably not a single development, in any case, an organized course of action of smart meters, trades frameworks, and data the chief’s systems that engage two-route correspondence among utilities and customers. Utilities and purchasers can comprehend their power use designs from the point-by-point examination of meter information. Along these lines, it is both practical and green [8]. It is conceivable to profit by request flexibility and better decisions on tax plans In this way, determining gives the clients the way to relate power use conduct with utilization cost. Deep extreme machine learning technique is a data handling framework enlivened by the way natural apprehensive frameworks process the data
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