Synthetic Power

Synthetic power data generation is emerging as a crucial tool to address the limitations of real-world data scarcity in various scientific domains. Current research focuses on developing advanced generative models, such as diffusion models and generative adversarial networks, to create realistic synthetic power data for energy consumption, cognitive neuroimaging, and renewable energy forecasting. These models leverage metadata (e.g., location, weather, building type) to improve data quality and realism, enabling more robust statistical analyses and machine learning applications. The availability of high-quality synthetic datasets is significantly advancing research by providing access to large, diverse, and readily available data for studies where real data is limited or expensive to acquire.