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Generating scenarios from probabilistic short-term load forecasts via non-linear Bayesian regression

Generating scenarios from probabilistic short-term load forecasts via non-linear Bayesian regression

Category
Academic chapter/article/Conference paper
Abstract
In this paper we present a simple and intuitive method for fitting a non-linear Bayesian regression model on short-term load forecasts. Such models have been implemented via Bayesian neural networks, which are known for their hyper-parameter sensitivity. We instead show a more general method to fit any regression model and demonstrate this by using a tree-model. Further, we evaluate the results against non-linear quantile regression, a common technique in probabilistic load forecasting. The resulting model allows to generate samples for future scenarios and thus can be applied to operations problems such as dynamic control of battery storage, an application that quantile regression is unfit for.
Client
  • Research Council of Norway (RCN) / 257626
Language
English
Affiliation
  • SINTEF Energy Research / Energisystemer
  • Norwegian University of Science and Technology
Year
Publisher
IEEE
Book
2021 International Conference on Smart Energy Systems and Technologies - SEST
ISBN
978-1-7281-7660-4