Power Data

Power data analysis focuses on extracting meaningful information from power measurements across diverse applications, aiming to improve efficiency, reliability, and sustainability. Current research employs machine learning techniques, including neural networks and Bayesian methods, to model complex relationships within power data, such as predicting carbon intensity in datacenters or estimating soiling ratios in solar panels. These advancements enable more accurate forecasting, optimized resource management, and improved understanding of energy systems, ultimately contributing to more efficient and environmentally responsible energy production and consumption.