Securing Data Transmission with Genetic Parser and Deep Residual Bivariate Pascal Values for Energy Efficiency and Anonymity for Smart Grids

To address the growing demands of data transmission, we present a novel method that balances energy efficiency and data integrity. For satisfying the ever-increasing requirements of data transmission, it is essential to locate the optimal balance between energy efficiency and integrity. When working with older technology, it is not uncommon for one of these two factors to prove to be more important than the other. An innovative method for optimising the efficiency of data transmission is presented by us. This method makes use of genetic parser-based techniques. We also deal with complexity, which is a significant issue with most of the modern methods of data collection. Through the incorporation of deep residual learning into our framework, which enables us to overcome this challenge, we can enhance the system's capability to analyse and process intricate data patterns. The proposed work introduces genetic parser-based techniques, deep residual learning, and Bivariate Pascal Values to optimize data transmission efficiency, handle complexity, and enhance security in smart grids. Our approach employs genetic parser-based techniques and deep residual learning to optimize data transmission efficiency and reduce complexity. By incorporating bivariate Pascal values, our method enhances the security of transmitted data. Deep residual learning, parser-based techniques, and bivariate Pascal Values have been utilised in the development of a sophisticated method that protects transmitted data while simultaneously reducing complexity. The effectiveness of the strategy is tested against IEEE 30 bus that was presented is demonstrated by the successful reduction of energy usage without compromising the safety and security of the data. Our method achieved a 20% reduction in energy consumption while maintaining data security, demonstrating its effectiveness in optimizing data transmission.