The energy consumption is one of the significant challenges of smart grid networks, since it has nonlinear
and changing trends, high computational complexity of the conventional forecasting systems, and
uncertainty in the data. The existing machine learning and statistical forecasting methods do not often
exhibit high prediction accuracy, do not extract enough features, and cannot adapt to changing smart grid
systems, thus obstructing energy management, load balancing, and effective transportation of energy.
Effective forecasting is crucial in intelligent energy systems, as it can help minimize energy losses, ensure
grid stability, enable demand response initiatives, and optimize sustainable energy use. To overcome
these drawbacks, the study proposes a novel Advanced Pattern Computation on Reinforcement Learning
and Metaheuristic Optimization of Energy Consumption Forecasting in Smart grids Networks. The
suggested framework is founded on the Reinforcement Learning methods to identify sequential energy
consumption behavior and adaptively learn active energy consumption patterns based on smart meter
data. Moreover, a Metaheuristic Optimization strategy is incorporated in order to optimize the selection
of features, prediction parameters and maximize prediction efficacy and minimize computational cost.
The framework will be able to model the latent temporal relationships and complex trends in energy
consumption in various smart grid conditions. The smart grid energy consumption data was assessed
with a publicly available data of residential and industrial electricity consumption. The experimental
findings were high accuracy (98.36%), precision (97.82%), recall (97.44%), F1-score (97.63%), and loss
(0.048), which meant that it had great forecasting accuracy, better grid stability, and intelligent
performance in managing energy.