Energy systems
Graph-based deep learning models can act as powerful tools for analyzing and optimizing complex energy systems. By representing energy networks as graphs, these models can capture intricate relationships and dependencies between different components, enabling more accurate predictions, efficient control strategies, and improved decision-making.
Graph representation of energy systems
Energy systems can be naturally represented as graphs, where nodes typically represent physical components such as generators, transformers, transmission lines, and loads, while edges represent connections and interactions between these components. This graph structure allows us to model the following:
- Power flow between components
- Interdependencies in the network
- Spatial and temporal relationships
- System topology and connectivity
Load forecasting
Graph-based deep learning models have shown superior performance in predicting electricity demand across power...
