Privacy Preserving Ultra-Short-term Wind Power Prediction Based on Secure Multi Party Computation

The project represents a niche academic application of Secure Multi-Party Computation (MPC) to renewable energy forecasting. While the problem (data silos in energy) is real, the project has zero stars and no community traction despite being over three years old. It functions primarily as a reference implementation for a specific research paper (arXiv:2301.13513v1). Defensibility is very low because it is an implementation of known MPC protocols (like SPDZ or ABY3) applied to standard spatial-temporal neural networks. A competitor could replicate the logic from the paper in a few weeks. Frontier labs like OpenAI or Google are unlikely to enter the 'ultra-short-term wind power prediction' market directly, making frontier risk low. However, the project faces high displacement risk from more efficient privacy-preserving techniques like Federated Learning (FL) or Trusted Execution Environments (TEEs), which generally offer better performance than pure MPC for high-velocity time-series data like wind output.