Implementing a Hybrid Deep Learning Model to Identify Critical Factors for Energy Efficiency in Smart Grids

Abstract

The creation and use of a mixed deep learning model in the smart grid system to increase energy efficiency and environmental friendliness is discussed in this paper Considering how complex energy consumption patterns might be, the model is designed to forecast simultaneously heating and cooling demands. It employs cutting-edge deep learning approaches like attention processes to simplify things, recurrent layers for temporal relationships, and convolutional layers for feature extraction. Combining these elements not only opens doors to stakeholders but also enables the mix model to provide reliable forecasts. For applications in the smart grid, this makes it a valuable instrument. Part of this approach involves designing a multi-output neural network, cleaning up energy data ahead of time, and verifying the model's effectiveness using significant criteria such Mean Absolute Error (MAE) and loss. The hybrid model performs better than conventional neural networks according the findings. On the practice and test environments, it greatly reduces errors. By focussing on the little details, one may get crucial knowledge about the elements influencing energy pricing. To accommodate various smart grid configurations, the model may also be raised or lowered. This facilitates quick judgements and most effective use of resources. Although the proposed mixed model represents a significant advance in smart grid analytics, many issues still exist like the need for increased processing capability and minor proof adjustments. This research aids to improve smart energy systems in keeping with the objectives of sustainable energy management. Modern grids therefore become more flexible and resilient as well as stronger. This study prepares the basis for further studies with fresh elements like employment trends and weather. Future projections will therefore be increasingly more reliable and accurate.