Seeker Optimized Deep Neural Model for Pattern Mining in Smart Grid Energy

Smart grids collect a tremendous amount of energy-use data in various formats from smart meters, distributed sensors, renewable energy systems, and IoT monitoring systems. However, existing pattern mining techniques using deep neural networks are complex, and feature optimization and predictive performance are lacking for dynamic and non-linear energy usage patterns. That restricts the ability to forecast energy in real time, balance energy loads, detect faults, and manage energy demand and response, resulting in energy loss, instability and inefficient utilization of energy resources. Therefore, an intelligent, optimized pattern-mining framework is required to improve the operational efficiency and precision of decision-making in the smart grid. In the work, a Seeker Optimized Deep Neural Model for Pattern Mining in Smart Grid Energy is proposed to address the above challenges. The proposed scheme includes a Deep Neural Network (DNN) for improved feature selection and weight optimization, along with efficient learning of the hidden layers and the Seeker Optimization Algorithm. The technique of Seeker optimization strategy is a very efficient method of obtaining an optimum energy consumption pattern, which decreases the training time and convergence error. It enhances the classification accuracy, removes unnecessary energy features, and allows adaptive learning in changing energy conditions, which will improve smart grid analytics. Moreover, the proposed algorithm can effectively identify abnormal consumption patterns and optimize the management of integration renewable energy. The proposed model is tested with reference energy data sets available from smart meters and Energy Consumption Repository. The accuracy, precision, recall, F1-score, energy prediction rate, and computational time are used for the evaluation measures. The experimental results reveal that the proposed Seeker Optimized DNN technique outperforms the conventional machine learning and deep learning methods in terms of pattern-mining accuracy, convergence speed and error rates. The results show the energy savings, energy monitoring, better demand prediction, and intelligent energy management of the next generation smart grid system.