TransCarbonNet: Multi-Day Grid Carbon Intensity Forecasting Using Hybrid Self-Attention and Bi-LSTM Temporal Fusion for Sustainable Energy Management

Sustainable energy systems will entail a change in the carbon intensity projections, which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions. The present article outlines the TransCarbonNet, a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory (Bi-LSTM) network to forecast the carbon intensity of the grid several days. The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data; hence, it is able to give suitable forecasts over a period of seven days. TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections, which means the Bi-LSTM element calculates sequential dependencies in both directions. Massive tests on two actual data sets indicate much improved results in comparison with the existing results, with mean relative errors of 15.3 percent and 12.7 percent, respectively. The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations, and informed decisions on the management of carbon sustainability. The effectiveness of the proposed solution has been validated in numerous cases of operations, and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.