Intelligent Modelling of Smart Grid by Fractal Analysis: Towards Scale Invariant Networking

Deep learning approaches are supposed to improve prediction accuracy by being stochastic and allowing bi-directional connections between neurons as a development of neural network-based prediction methods. This paper is an effort to model smart grid through pragmatic approach focusing on fractal analysis as a potential tool to mathematically formulate and assess the dataset and come with a solution which makes development of smart grids scale invariant. Additionally, this paper analyses solar energy generation in India and creates a mechanism to predict it with little uncertainty because solar-based power generation is a significant alternative for developing nations like India that can increase energy security, decrease global warming, and improve supply. All in all, this paper takes you through demand side management and also reducing uncertainty in renewable energy generation and help assist engineers in scaling devices to existing networks.