Matches in SemOpenAlex for { <https://semopenalex.org/work/W3128958724> ?p ?o ?g. }
Showing items 1 to 74 of
74
with 100 items per page.
- W3128958724 endingPage "179" @default.
- W3128958724 startingPage "172" @default.
- W3128958724 abstract "The data of the power Internet of Things (IOT) system is transferred from the IaaS layer to the SaaS layer. The general data preprocessing method mainly solves the problem of big data anomalies and missing at the PaaS layer, but it still lacks the ability to judge the high error data that meets the timing characteristics, making it difficult to deal with heterogeneous power inconsistent issues. This paper shows this phenomenon and its physical mechanism, showing the difficulty of building a quantitative model forward. A data-driven method is needed to form a hybrid model to correct the data. The research object is the electricity meter data on both sides of a commercial building transformer, which comes from different power IOT systems. The low-voltage side was revised based on the high-voltage side. Compared with the correction method based on purely using neural networks, the combined method, Linear Regression (LS) + Differential Evolution (DE) + Extreme Learning Machine (ELM), further reduces the deviation from approximately 4% to 1%." @default.
- W3128958724 created "2021-02-15" @default.
- W3128958724 creator A5053095481 @default.
- W3128958724 creator A5053468791 @default.
- W3128958724 creator A5068435914 @default.
- W3128958724 creator A5074339191 @default.
- W3128958724 creator A5080958374 @default.
- W3128958724 creator A5089637978 @default.
- W3128958724 date "2021-11-01" @default.
- W3128958724 modified "2023-09-30" @default.
- W3128958724 title "Data consistency method of heterogeneous power IOT based on hybrid model" @default.
- W3128958724 cites W1994734153 @default.
- W3128958724 cites W2016340027 @default.
- W3128958724 cites W2020683752 @default.
- W3128958724 cites W2803881197 @default.
- W3128958724 cites W3022392400 @default.
- W3128958724 cites W3095699399 @default.
- W3128958724 doi "https://doi.org/10.1016/j.isatra.2021.01.056" @default.
- W3128958724 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33563464" @default.
- W3128958724 hasPublicationYear "2021" @default.
- W3128958724 type Work @default.
- W3128958724 sameAs 3128958724 @default.
- W3128958724 citedByCount "0" @default.
- W3128958724 crossrefType "journal-article" @default.
- W3128958724 hasAuthorship W3128958724A5053095481 @default.
- W3128958724 hasAuthorship W3128958724A5053468791 @default.
- W3128958724 hasAuthorship W3128958724A5068435914 @default.
- W3128958724 hasAuthorship W3128958724A5074339191 @default.
- W3128958724 hasAuthorship W3128958724A5080958374 @default.
- W3128958724 hasAuthorship W3128958724A5089637978 @default.
- W3128958724 hasConcept C120314980 @default.
- W3128958724 hasConcept C121332964 @default.
- W3128958724 hasConcept C124101348 @default.
- W3128958724 hasConcept C154945302 @default.
- W3128958724 hasConcept C163258240 @default.
- W3128958724 hasConcept C2776436953 @default.
- W3128958724 hasConcept C34736171 @default.
- W3128958724 hasConcept C41008148 @default.
- W3128958724 hasConcept C50644808 @default.
- W3128958724 hasConcept C62520636 @default.
- W3128958724 hasConcept C89227174 @default.
- W3128958724 hasConcept C93361087 @default.
- W3128958724 hasConceptScore W3128958724C120314980 @default.
- W3128958724 hasConceptScore W3128958724C121332964 @default.
- W3128958724 hasConceptScore W3128958724C124101348 @default.
- W3128958724 hasConceptScore W3128958724C154945302 @default.
- W3128958724 hasConceptScore W3128958724C163258240 @default.
- W3128958724 hasConceptScore W3128958724C2776436953 @default.
- W3128958724 hasConceptScore W3128958724C34736171 @default.
- W3128958724 hasConceptScore W3128958724C41008148 @default.
- W3128958724 hasConceptScore W3128958724C50644808 @default.
- W3128958724 hasConceptScore W3128958724C62520636 @default.
- W3128958724 hasConceptScore W3128958724C89227174 @default.
- W3128958724 hasConceptScore W3128958724C93361087 @default.
- W3128958724 hasLocation W31289587241 @default.
- W3128958724 hasOpenAccess W3128958724 @default.
- W3128958724 hasPrimaryLocation W31289587241 @default.
- W3128958724 hasRelatedWork W1970154823 @default.
- W3128958724 hasRelatedWork W2024918697 @default.
- W3128958724 hasRelatedWork W2048053751 @default.
- W3128958724 hasRelatedWork W2137971267 @default.
- W3128958724 hasRelatedWork W2350879319 @default.
- W3128958724 hasRelatedWork W2353865532 @default.
- W3128958724 hasRelatedWork W2382404723 @default.
- W3128958724 hasRelatedWork W2390190248 @default.
- W3128958724 hasRelatedWork W2901917862 @default.
- W3128958724 hasRelatedWork W3010469175 @default.
- W3128958724 hasVolume "117" @default.
- W3128958724 isParatext "false" @default.
- W3128958724 isRetracted "false" @default.
- W3128958724 magId "3128958724" @default.
- W3128958724 workType "article" @default.