Matches in SemOpenAlex for { <https://semopenalex.org/work/W3047895919> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W3047895919 abstract "Machine learning and agent-based modeling are two popular tools in energy research. In this article, we propose an innovative methodology that combines these methods. For this purpose, we develop an electricity price forecasting technique using artificial neural networks and integrate the novel approach into the established agent-based electricity market simulation model PowerACE. In a case study covering ten interconnected European countries and a time horizon from 2020 until 2050 at hourly resolution, we benchmark the new forecasting approach against a simpler linear regression model as well as a naive forecast. Contrary to most of the related literature, we also evaluate the statistical significance of the superiority of one approach over another by conducting Diebold-Mariano hypothesis tests. Our major results can be summarized as follows. Firstly, in contrast to real-world electricity price forecasts, we find the naive approach to perform very poorly when deployed model-endogenously. Secondly, although the linear regression performs reasonably well, it is outperformed by the neural network approach. Thirdly, the use of an additional classifier for outlier handling substantially improves the forecasting accuracy, particularly for the linear regression approach. Finally, the choice of the model-endogenous forecasting method has a clear impact on simulated electricity prices. This latter finding is particularly crucial since these prices are a major results of electricity market models." @default.
- W3047895919 created "2020-08-13" @default.
- W3047895919 creator A5004540330 @default.
- W3047895919 creator A5026214374 @default.
- W3047895919 creator A5080499857 @default.
- W3047895919 creator A5089437817 @default.
- W3047895919 date "2020-01-01" @default.
- W3047895919 modified "2023-09-23" @default.
- W3047895919 title "The Merge of Two Worlds: Integrating Artificial Neural Networks into Agent-Based Electricity Market Simulation" @default.
- W3047895919 doi "https://doi.org/10.5445/ir/1000122364" @default.
- W3047895919 hasPublicationYear "2020" @default.
- W3047895919 type Work @default.
- W3047895919 sameAs 3047895919 @default.
- W3047895919 citedByCount "0" @default.
- W3047895919 crossrefType "journal-article" @default.
- W3047895919 hasAuthorship W3047895919A5004540330 @default.
- W3047895919 hasAuthorship W3047895919A5026214374 @default.
- W3047895919 hasAuthorship W3047895919A5080499857 @default.
- W3047895919 hasAuthorship W3047895919A5089437817 @default.
- W3047895919 hasConcept C119599485 @default.
- W3047895919 hasConcept C119857082 @default.
- W3047895919 hasConcept C127413603 @default.
- W3047895919 hasConcept C13280743 @default.
- W3047895919 hasConcept C146733006 @default.
- W3047895919 hasConcept C149782125 @default.
- W3047895919 hasConcept C154945302 @default.
- W3047895919 hasConcept C162324750 @default.
- W3047895919 hasConcept C185798385 @default.
- W3047895919 hasConcept C205649164 @default.
- W3047895919 hasConcept C206658404 @default.
- W3047895919 hasConcept C2781104810 @default.
- W3047895919 hasConcept C41008148 @default.
- W3047895919 hasConcept C50644808 @default.
- W3047895919 hasConceptScore W3047895919C119599485 @default.
- W3047895919 hasConceptScore W3047895919C119857082 @default.
- W3047895919 hasConceptScore W3047895919C127413603 @default.
- W3047895919 hasConceptScore W3047895919C13280743 @default.
- W3047895919 hasConceptScore W3047895919C146733006 @default.
- W3047895919 hasConceptScore W3047895919C149782125 @default.
- W3047895919 hasConceptScore W3047895919C154945302 @default.
- W3047895919 hasConceptScore W3047895919C162324750 @default.
- W3047895919 hasConceptScore W3047895919C185798385 @default.
- W3047895919 hasConceptScore W3047895919C205649164 @default.
- W3047895919 hasConceptScore W3047895919C206658404 @default.
- W3047895919 hasConceptScore W3047895919C2781104810 @default.
- W3047895919 hasConceptScore W3047895919C41008148 @default.
- W3047895919 hasConceptScore W3047895919C50644808 @default.
- W3047895919 hasLocation W30478959191 @default.
- W3047895919 hasOpenAccess W3047895919 @default.
- W3047895919 hasPrimaryLocation W30478959191 @default.
- W3047895919 hasRelatedWork W1992224998 @default.
- W3047895919 hasRelatedWork W2256372749 @default.
- W3047895919 hasRelatedWork W2315598830 @default.
- W3047895919 hasRelatedWork W2608814464 @default.
- W3047895919 hasRelatedWork W2627014064 @default.
- W3047895919 hasRelatedWork W2750304600 @default.
- W3047895919 hasRelatedWork W2924259487 @default.
- W3047895919 hasRelatedWork W2948535221 @default.
- W3047895919 hasRelatedWork W2963460248 @default.
- W3047895919 hasRelatedWork W2988271430 @default.
- W3047895919 hasRelatedWork W2990839525 @default.
- W3047895919 hasRelatedWork W2991407010 @default.
- W3047895919 hasRelatedWork W3003780338 @default.
- W3047895919 hasRelatedWork W3016608500 @default.
- W3047895919 hasRelatedWork W3068571712 @default.
- W3047895919 hasRelatedWork W3125238555 @default.
- W3047895919 hasRelatedWork W3161889436 @default.
- W3047895919 hasRelatedWork W46513916 @default.
- W3047895919 hasRelatedWork W93253360 @default.
- W3047895919 hasRelatedWork W2188561544 @default.
- W3047895919 isParatext "false" @default.
- W3047895919 isRetracted "false" @default.
- W3047895919 magId "3047895919" @default.
- W3047895919 workType "article" @default.