Matches in SemOpenAlex for { <https://semopenalex.org/work/W3045116473> ?p ?o ?g. }
Showing items 1 to 93 of
93
with 100 items per page.
- W3045116473 endingPage "3750" @default.
- W3045116473 startingPage "3750" @default.
- W3045116473 abstract "To realize the distributed generation and to make the partnership between the dispatchable units and variable renewable resources work efficiently, accurate and flexible monitoring needs to be implemented. Due to digital transformation in the energy industry, a large amount of data is and will be captured every day, but the inability to process them in real time challenges the conventional monitoring and maintenance practices. Access to automated and reliable data-filtering tools seems to be crucial for the monitoring of many distributed generation units, avoiding false warnings and improving the reliability. This study aims to evaluate a machine-learning-based methodology for autodetecting outliers from real data, exploring an interdisciplinary solution to replace the conventional manual approach that was very time-consuming and error-prone. The raw data used in this study was collected from experiments on a 100-kW micro gas turbine test rig in Norway. The proposed method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect and filter out the outliers. The filtered datasets are used to develop artificial neural networks (ANNs) as a baseline to predict the normal performance of the system for monitoring applications. Results show that the filtering method presented is reliable and fast, minimizing time and resources for data processing. It was also shown that the proposed method has the potential to enhance the performance of the predictive models and ANN-based monitoring." @default.
- W3045116473 created "2020-07-29" @default.
- W3045116473 creator A5004033723 @default.
- W3045116473 creator A5020796392 @default.
- W3045116473 creator A5052960910 @default.
- W3045116473 creator A5057630867 @default.
- W3045116473 creator A5071282676 @default.
- W3045116473 creator A5086178863 @default.
- W3045116473 date "2020-07-21" @default.
- W3045116473 modified "2023-09-26" @default.
- W3045116473 title "Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning" @default.
- W3045116473 cites W1980913991 @default.
- W3045116473 cites W1989724844 @default.
- W3045116473 cites W1993533808 @default.
- W3045116473 cites W1995443851 @default.
- W3045116473 cites W2015755712 @default.
- W3045116473 cites W2018114227 @default.
- W3045116473 cites W2020440301 @default.
- W3045116473 cites W2039869729 @default.
- W3045116473 cites W2068602541 @default.
- W3045116473 cites W2083060736 @default.
- W3045116473 cites W2089955789 @default.
- W3045116473 cites W2126703473 @default.
- W3045116473 cites W2570051910 @default.
- W3045116473 cites W2609597778 @default.
- W3045116473 cites W2765863015 @default.
- W3045116473 cites W2770846092 @default.
- W3045116473 cites W2774046096 @default.
- W3045116473 cites W2805975301 @default.
- W3045116473 cites W2899039436 @default.
- W3045116473 doi "https://doi.org/10.3390/en13143750" @default.
- W3045116473 hasPublicationYear "2020" @default.
- W3045116473 type Work @default.
- W3045116473 sameAs 3045116473 @default.
- W3045116473 citedByCount "4" @default.
- W3045116473 countsByYear W30451164732021 @default.
- W3045116473 countsByYear W30451164732022 @default.
- W3045116473 crossrefType "journal-article" @default.
- W3045116473 hasAuthorship W3045116473A5004033723 @default.
- W3045116473 hasAuthorship W3045116473A5020796392 @default.
- W3045116473 hasAuthorship W3045116473A5052960910 @default.
- W3045116473 hasAuthorship W3045116473A5057630867 @default.
- W3045116473 hasAuthorship W3045116473A5071282676 @default.
- W3045116473 hasAuthorship W3045116473A5086178863 @default.
- W3045116473 hasBestOaLocation W30451164731 @default.
- W3045116473 hasConcept C111919701 @default.
- W3045116473 hasConcept C119857082 @default.
- W3045116473 hasConcept C121332964 @default.
- W3045116473 hasConcept C124101348 @default.
- W3045116473 hasConcept C154945302 @default.
- W3045116473 hasConcept C163258240 @default.
- W3045116473 hasConcept C41008148 @default.
- W3045116473 hasConcept C43214815 @default.
- W3045116473 hasConcept C50644808 @default.
- W3045116473 hasConcept C62520636 @default.
- W3045116473 hasConcept C73555534 @default.
- W3045116473 hasConcept C79337645 @default.
- W3045116473 hasConcept C98045186 @default.
- W3045116473 hasConceptScore W3045116473C111919701 @default.
- W3045116473 hasConceptScore W3045116473C119857082 @default.
- W3045116473 hasConceptScore W3045116473C121332964 @default.
- W3045116473 hasConceptScore W3045116473C124101348 @default.
- W3045116473 hasConceptScore W3045116473C154945302 @default.
- W3045116473 hasConceptScore W3045116473C163258240 @default.
- W3045116473 hasConceptScore W3045116473C41008148 @default.
- W3045116473 hasConceptScore W3045116473C43214815 @default.
- W3045116473 hasConceptScore W3045116473C50644808 @default.
- W3045116473 hasConceptScore W3045116473C62520636 @default.
- W3045116473 hasConceptScore W3045116473C73555534 @default.
- W3045116473 hasConceptScore W3045116473C79337645 @default.
- W3045116473 hasConceptScore W3045116473C98045186 @default.
- W3045116473 hasIssue "14" @default.
- W3045116473 hasLocation W30451164731 @default.
- W3045116473 hasLocation W30451164732 @default.
- W3045116473 hasOpenAccess W3045116473 @default.
- W3045116473 hasPrimaryLocation W30451164731 @default.
- W3045116473 hasRelatedWork W1591209867 @default.
- W3045116473 hasRelatedWork W2337929971 @default.
- W3045116473 hasRelatedWork W2783242366 @default.
- W3045116473 hasRelatedWork W3183283580 @default.
- W3045116473 hasRelatedWork W4250175685 @default.
- W3045116473 hasRelatedWork W4283741549 @default.
- W3045116473 hasRelatedWork W4312609022 @default.
- W3045116473 hasRelatedWork W4313069709 @default.
- W3045116473 hasRelatedWork W1629725936 @default.
- W3045116473 hasRelatedWork W2575052681 @default.
- W3045116473 hasVolume "13" @default.
- W3045116473 isParatext "false" @default.
- W3045116473 isRetracted "false" @default.
- W3045116473 magId "3045116473" @default.
- W3045116473 workType "article" @default.