dc.contributor.author | Löschenbrand, Markus | |
dc.contributor.author | Gros, Sebastien | |
dc.contributor.author | Lakshmanan, Venkatachalam | |
dc.date.accessioned | 2021-09-29T10:51:02Z | |
dc.date.available | 2021-09-29T10:51:02Z | |
dc.date.created | 2021-09-28T14:12:16Z | |
dc.date.issued | 2021 | |
dc.identifier.isbn | 978-1-7281-7660-4 | |
dc.identifier.uri | https://hdl.handle.net/11250/2785983 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.relation.ispartof | 2021 International Conference on Smart Energy Systems and Technologies - SEST | |
dc.title | Generating scenarios from probabilistic short-term load forecasts via non-linear Bayesian regression | en_US |
dc.type | Chapter | en_US |
dc.description.version | acceptedVersion | en_US |
dc.rights.holder | © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.identifier.doi | 10.1109/SEST50973.2021.9543288 | |
dc.identifier.cristin | 1939861 | |
dc.relation.project | Norges forskningsråd: 257626 | en_US |
cristin.ispublished | true | |
cristin.fulltext | postprint | |
cristin.qualitycode | 1 | |