Predictive Modeling of Terminal Node Eligibility Assessment in Power Load Management Using Neural Network

Efficient management of electrical loads is pivotal in optimizing power distribution and ensuring stability within power grids. A critical aspect of this process involves accurately determining the eligibility of terminal nodes to maintain seamless operation and support. This paper introduces a novel approach, employing fully connected Neural Network, to assess the eligibility of terminal nodes based on five key attributes: RAM, Storage, Trust Factor, Distance, and Transmission Rate. We have designed a sequential model architecture consisting of multiple dense layers, each followed by ReLU activation, culminating in a linear output that classifies nodes as eligible or not. The dataset comprises historical data points from various nodes within an operational power load management system, which have been standardized and normalized to aid in effective model training and validation. Our results demonstrate that the proposed Neural Network model achieves high accuracy and robustness in predicting node eligibility, offering significant improvements over traditional statistical methods. Additionally, this study not only enhances the understanding of node behavior within electrical grids but also paves the way for more sophisticated AI-driven approaches in energy systems management.