IoT-Based Energy Efficiency and Management

Abstract

The increasing complexity of energy requirements in the residential and industrial sphere has added stress to the need of scalable and autonomous solutions to ensure efficient handling of energy. Scalability is also difficult in scenarios where energy consumption is diffuse and changing, especially those sectors of industry where power usage at distant locations must be observed on an ongoing basis. The unending automation regulations created based on the consumption pattern would require immediate usages in real condition without reliance on maintenance by human beings. The system should be updated automatically in order to have optimal energy configurations that should contain operational best practices in coping with network topology change as well. The proposed solution takes up these issues through extensive study using Internet of Things (IoT) frameworks and machine learning-based approaches. The system has dynamically network energy supervisory capabilities with distributed system energy management capabilities through its flexible system architecture. Industry standards are shaped around research-based behavioural patterns to act as a device classification mechanism and to maximise on resource distribution. Both smart industry energy managers and autonomous smart home systems will be able to track and manage various equipment of operation using the centralized system but unlike that of the autonomous smart home systems which without any human assistance reduces wastes. The combination of IoT and data analytics as a working process serves as a tool to monitor the industrial energy consumption and reduce the resources overpower and contribute to the ecological processes in all the environments.