Explainable AI-driven energy forecasting: A DGMR-based feature extraction and EGST-Net prediction framework for transparent decision-making

Transparency in Decision-Making the modern energy management systems, Transparency in Decision-Making requires proper and understandable forecasting models capable of controlling and documenting the intricate dynamics of consumption when implemented within the smart grid scenario. However, the existing energy prediction techniques, including traditional statistical models and independent deep learning techniques, are often incapable of learning a nonlinear spatiotemporal relationship, and offer poor interpretability, resulting in a reduction of reliability to real-life use. This paper proposes an Explainable AI-based energy forecasting model to address these limitations, which will be a combination of the Dynamic Gated Memory Refinement (DGMR) and a hybrid EGST-Net framework comprising Convolutional Long Short-Term Memory (ConvLSTM) and Transformer-based attention models. The suggested method is novel in a sense that it is adaptably capable of refining feature of its aim that chooses selectively, in informative temporal-spatial patterns and eliminates noise, and an attention-driven elucidation section that enhances intelligibility in the determination to make forecasts.The framework conducts the multi-stage processing of the data (data preprocessing, DGMR-based feature extraction, spatiotemporal learning with the help of EGST-Net, and attention-based interpretability analysis) and was launched on the Python environment with deep learning libraries to provide effective training and testing of the model. The experimental findings showed better performance than the traditional baseline models with a prediction accuracy of 96% and little forecasting error scores. The suggested system will appeal to energy providers, smart grid operators, policymakers, and other industrial stakeholders as it can facilitate resource allocation and efficient demand prediction, as well as make the decisions made by AI interpretable. All in all, the framework is part of the achievement of intelligent, transparent, and scalable energy management solutions to the development of next generation smart energy infrastructures.