A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.
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An online regularized multivariate distributional regression method is introduced for high-dimensional probabilistic electricity price forecasting, with a case study on German day-ahead data and an open-source implementation.
A laminar cyber-physical design with standardized interfaces can translate device-level flexibility into reliable grid services across scales, as illustrated by U.S. and Danish pilots and operational deployments.
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Online forecast reconciliation using linear models
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.