The Flow has not summarised this story yet — read it at arXiv Machine Learning.
CoCopula is a framework that transforms site-level probabilistic forecasts into reliable fleet-level forecasts by combining Copula-based dependence modeling with Context-Aware Conformal Prediction. It captures cross-site correlations and mitigates aggregation-induced miscalibration, producing sharp prediction intervals with near-nominal empirical coverage. Experiments on large-scale solar generation data from MISO, ERCOT, and SPP show lower Winkler scores than competing baselines and performance comparable to directly calibrated system-level forecasts.
arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.
arXiv:2609.23590v1 Announce Type: new Abstract: Battery energy storage system (BESS) dispatch must preserve operational feasibility while declining price spreads reduce the margin available to pay fo...
arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.
arXiv:2610.07232v1 Announce Type: new Abstract: Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation...
arXiv:2609.06656v1 Announce Type: cross Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load foreca...