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.