Empirical Analysis of Advanced Power Management with Smart Grid Using IoT & Machine Learning
Abstract
Although with the use of IoT and machine learning in the traditional grid opens new opportunities. There are a few challenges that have constrained their growth. Some of the challenges are discussed here which can open the door for new researches. When we implement IoT, sensors are the basic component which is used to monitor the transmission line, and they depend on battery life so some energy harvesting technique could be implemented. While information is to send to and forth from the smart meter to cloud through a communication network, packet loss and congestion is a challenging issue. Communication protocol should be chosen to minimize the packet loss and delay in a packet. The congestion in a network causes a delay in packets, it degrades the SG performance sometimes exceeds the maximum tolerable delay. So, it is necessary to optimize network design by integrating the minimum number of gateways, and IoT components. To date, there is no unified standard defined for the smart grid, which causes reliability, interoperability, and security issues, So, there is a need for unified standardization. In this paper, we will explore all these challenges in smart grid technology.