Smart Grid Energy Trading Mechanisms: Leveraging Blockchain and Deep Learning for Sustainable Power Supply
U Ragavee, S. Sharadh Sureshbabu · 2025
The work tackles key problems in smart grids, namely, advanced demand management and real time trading, by linking blockchain and Long Short-Term Memory for learning automatic generators scheduling in smart grids. The current approaches do not consider modern energy consumption details and real time processing needs. This work defines a new model with blockchain to support the secure recording of each transaction, and with LSTM for prediction of energy demand. The innovative integration deals with high frequency trading, improves grid performance and environmentally manages energy resources. Predictability of 98.2% is shown and it can be used to both improve energy trading stability and grid reliability. The proposed system uses blockchain platform as a secure environment and LSTM as a demand estimator to overcome the inefficiencies of existing smart grid management with a scalable solution. By integrating the two, it facilitates better energy grid efficiency and contributes to a better sustainable energy environment that deals with technical and environmental obstacles in the smart grid.