FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3113309477> ?p ?o ?g. }

• W3113309477 endingPage "33" @default.
• W3113309477 startingPage "23" @default.
• W3113309477 abstract "Abstract Conventionally, researchers conducted field tests and modeling works for the Variable Refrigerant Flow system separately. In this study, we used the compressor model as a case study to illustrate a novel approach to integrate field tests and modeling works. Field tests in an office building were conducted to collect data. For mass flow rate prediction, three traditional models, including the 20-coefficient model, the efficiency-based model, and the efficiency-based 20-coefficient model, and three machine-learning-based models including Support Vector Regression, Neural Network, and Random Forest, were investigated and compared. We found that the efficiency-based 20-coefficient model and the Support Vector Regression model had a higher accuracy (the mean relative errors were 0.11% and 0.15%, and the coefficient of variation of the root mean-square-error were 0.20 and 0.23) and lower uncertainty within 0.2 g•s−1 for steady-state operation prediction. However, all six models failed to predict accurately the beginning part of the transient process. A tens-of-seconds delay existed between the predicted values and the experiment values. We applied the Convolutional-Neural-Network-based model to address this problem. The mean relative error of this model is reduced to 2% for dynamic simulation. In summary, we recommend the efficiency-based 20-coefficient model, and the Support Vector Regression model for the steady-state compressor model development, while the Convolutional-Neural-Network-based model is recommended for transient model development. For the power consumption prediction, 20-coefficient and Neural Network models can predict transient data well. The process of predicting the capacity is the same as the process of mass flow rate prediction." @default.
• W3113309477 created "2020-12-21" @default.
• W3113309477 creator A5014854303 @default.
• W3113309477 creator A5016656657 @default.
• W3113309477 creator A5024900649 @default.
• W3113309477 creator A5027204382 @default.
• W3113309477 creator A5042156236 @default.
• W3113309477 date "2021-03-01" @default.
• W3113309477 modified "2023-09-29" @default.
• W3113309477 title "Machine-learning-based compressor models: A case study for variable refrigerant flow systems" @default.
• W3113309477 cites W1120135930 @default.
• W3113309477 cites W1982209620 @default.
• W3113309477 cites W1999489246 @default.
• W3113309477 cites W2021885824 @default.
• W3113309477 cites W2028164787 @default.
• W3113309477 cites W2033509349 @default.
• W3113309477 cites W2038013852 @default.
• W3113309477 cites W2073980033 @default.
• W3113309477 cites W2093767424 @default.
• W3113309477 cites W2103550798 @default.
• W3113309477 cites W2404692435 @default.
• W3113309477 cites W2524895498 @default.
• W3113309477 cites W2538405680 @default.
• W3113309477 cites W2582381598 @default.
• W3113309477 cites W2595996626 @default.
• W3113309477 cites W2605782971 @default.
• W3113309477 cites W2792910464 @default.
• W3113309477 cites W2806625047 @default.
• W3113309477 cites W2806854509 @default.
• W3113309477 cites W2923654048 @default.
• W3113309477 cites W2938814897 @default.
• W3113309477 cites W2969466286 @default.
• W3113309477 cites W2969591044 @default.
• W3113309477 cites W2997363395 @default.
• W3113309477 cites W2999180071 @default.
• W3113309477 cites W3006687028 @default.
• W3113309477 cites W3011744717 @default.
• W3113309477 cites W3037541733 @default.
• W3113309477 cites W4248831067 @default.
• W3113309477 doi "https://doi.org/10.1016/j.ijrefrig.2020.12.003" @default.
• W3113309477 hasPublicationYear "2021" @default.
• W3113309477 type Work @default.
• W3113309477 sameAs 3113309477 @default.
• W3113309477 citedByCount "8" @default.
• W3113309477 countsByYear W31133094772021 @default.
• W3113309477 countsByYear W31133094772022 @default.
• W3113309477 countsByYear W31133094772023 @default.
• W3113309477 crossrefType "journal-article" @default.
• W3113309477 hasAuthorship W3113309477A5014854303 @default.
• W3113309477 hasAuthorship W3113309477A5016656657 @default.
• W3113309477 hasAuthorship W3113309477A5024900649 @default.
• W3113309477 hasAuthorship W3113309477A5027204382 @default.
• W3113309477 hasAuthorship W3113309477A5042156236 @default.
• W3113309477 hasConcept C121332964 @default.
• W3113309477 hasConcept C127413603 @default.
• W3113309477 hasConcept C131097465 @default.
• W3113309477 hasConcept C134306372 @default.
• W3113309477 hasConcept C182365436 @default.
• W3113309477 hasConcept C199499590 @default.
• W3113309477 hasConcept C33923547 @default.
• W3113309477 hasConcept C38349280 @default.
• W3113309477 hasConcept C41008148 @default.
• W3113309477 hasConcept C57879066 @default.
• W3113309477 hasConcept C78519656 @default.
• W3113309477 hasConceptScore W3113309477C121332964 @default.
• W3113309477 hasConceptScore W3113309477C127413603 @default.
• W3113309477 hasConceptScore W3113309477C131097465 @default.
• W3113309477 hasConceptScore W3113309477C134306372 @default.
• W3113309477 hasConceptScore W3113309477C182365436 @default.
• W3113309477 hasConceptScore W3113309477C199499590 @default.
• W3113309477 hasConceptScore W3113309477C33923547 @default.
• W3113309477 hasConceptScore W3113309477C38349280 @default.
• W3113309477 hasConceptScore W3113309477C41008148 @default.
• W3113309477 hasConceptScore W3113309477C57879066 @default.
• W3113309477 hasConceptScore W3113309477C78519656 @default.
• W3113309477 hasFunder F4320310578 @default.
• W3113309477 hasLocation W31133094771 @default.
• W3113309477 hasOpenAccess W3113309477 @default.
• W3113309477 hasPrimaryLocation W31133094771 @default.
• W3113309477 hasRelatedWork W140598204 @default.
• W3113309477 hasRelatedWork W1523280775 @default.
• W3113309477 hasRelatedWork W1999844873 @default.
• W3113309477 hasRelatedWork W2026759741 @default.
• W3113309477 hasRelatedWork W2030991462 @default.
• W3113309477 hasRelatedWork W2379972275 @default.
• W3113309477 hasRelatedWork W2505241250 @default.
• W3113309477 hasRelatedWork W3035577928 @default.
• W3113309477 hasRelatedWork W3041914721 @default.
• W3113309477 hasRelatedWork W3047895824 @default.
• W3113309477 hasVolume "123" @default.
• W3113309477 isParatext "false" @default.
• W3113309477 isRetracted "false" @default.
• W3113309477 magId "3113309477" @default.
• W3113309477 workType "article" @default.