Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313459210> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W4313459210 abstract "Load forecasting is an essential task in the power industry as an important means to assist the grid to balance supply demand. A large amount of user data monitored by smart grids can support deep learning models for load prediction, but accurate and fine-grained user data may reveal consumers' electricity consumption behaviors, which brings privacy and security issues. Federated Learning (FL) is a new type of high-efficiency machine learning between multiple participants or multiple computing nodes under the premise of ensuring information security during big data exchange and protecting the privacy of terminal data and personal data. Therefore, this paper explored a short-term residential energy demand forecasting method based on FL. The experimental data comes from the U.S. hourly residential base load. The federal forecast model was built on Pytorch, and we explored model behavior under different experimental conditions." @default.
- W4313459210 created "2023-01-06" @default.
- W4313459210 creator A5001594614 @default.
- W4313459210 creator A5018951250 @default.
- W4313459210 creator A5043042095 @default.
- W4313459210 creator A5068297028 @default.
- W4313459210 creator A5071672663 @default.
- W4313459210 creator A5076211304 @default.
- W4313459210 date "2022-10-24" @default.
- W4313459210 modified "2023-10-17" @default.
- W4313459210 title "Residential Short Term Load Forecasting Based on Federated Learning" @default.
- W4313459210 cites W2593117036 @default.
- W4313459210 cites W2601171548 @default.
- W4313459210 cites W2751802138 @default.
- W4313459210 cites W2754252319 @default.
- W4313459210 cites W2809833063 @default.
- W4313459210 cites W3044162788 @default.
- W4313459210 cites W3045941597 @default.
- W4313459210 cites W3104996215 @default.
- W4313459210 cites W3135032840 @default.
- W4313459210 cites W4285108231 @default.
- W4313459210 doi "https://doi.org/10.1109/dtpi55838.2022.9998969" @default.
- W4313459210 hasPublicationYear "2022" @default.
- W4313459210 type Work @default.
- W4313459210 citedByCount "1" @default.
- W4313459210 countsByYear W43134592102023 @default.
- W4313459210 crossrefType "proceedings-article" @default.
- W4313459210 hasAuthorship W4313459210A5001594614 @default.
- W4313459210 hasAuthorship W4313459210A5018951250 @default.
- W4313459210 hasAuthorship W4313459210A5043042095 @default.
- W4313459210 hasAuthorship W4313459210A5068297028 @default.
- W4313459210 hasAuthorship W4313459210A5071672663 @default.
- W4313459210 hasAuthorship W4313459210A5076211304 @default.
- W4313459210 hasConcept C10558101 @default.
- W4313459210 hasConcept C119599485 @default.
- W4313459210 hasConcept C121332964 @default.
- W4313459210 hasConcept C124101348 @default.
- W4313459210 hasConcept C127413603 @default.
- W4313459210 hasConcept C138885662 @default.
- W4313459210 hasConcept C193809577 @default.
- W4313459210 hasConcept C206658404 @default.
- W4313459210 hasConcept C2778023277 @default.
- W4313459210 hasConcept C2779438525 @default.
- W4313459210 hasConcept C41008148 @default.
- W4313459210 hasConcept C41895202 @default.
- W4313459210 hasConcept C42475967 @default.
- W4313459210 hasConcept C61797465 @default.
- W4313459210 hasConcept C62520636 @default.
- W4313459210 hasConcept C67186912 @default.
- W4313459210 hasConcept C75684735 @default.
- W4313459210 hasConcept C77088390 @default.
- W4313459210 hasConceptScore W4313459210C10558101 @default.
- W4313459210 hasConceptScore W4313459210C119599485 @default.
- W4313459210 hasConceptScore W4313459210C121332964 @default.
- W4313459210 hasConceptScore W4313459210C124101348 @default.
- W4313459210 hasConceptScore W4313459210C127413603 @default.
- W4313459210 hasConceptScore W4313459210C138885662 @default.
- W4313459210 hasConceptScore W4313459210C193809577 @default.
- W4313459210 hasConceptScore W4313459210C206658404 @default.
- W4313459210 hasConceptScore W4313459210C2778023277 @default.
- W4313459210 hasConceptScore W4313459210C2779438525 @default.
- W4313459210 hasConceptScore W4313459210C41008148 @default.
- W4313459210 hasConceptScore W4313459210C41895202 @default.
- W4313459210 hasConceptScore W4313459210C42475967 @default.
- W4313459210 hasConceptScore W4313459210C61797465 @default.
- W4313459210 hasConceptScore W4313459210C62520636 @default.
- W4313459210 hasConceptScore W4313459210C67186912 @default.
- W4313459210 hasConceptScore W4313459210C75684735 @default.
- W4313459210 hasConceptScore W4313459210C77088390 @default.
- W4313459210 hasFunder F4320335777 @default.
- W4313459210 hasLocation W43134592101 @default.
- W4313459210 hasOpenAccess W4313459210 @default.
- W4313459210 hasPrimaryLocation W43134592101 @default.
- W4313459210 hasRelatedWork W1498762745 @default.
- W4313459210 hasRelatedWork W1589973774 @default.
- W4313459210 hasRelatedWork W2023642087 @default.
- W4313459210 hasRelatedWork W2074513827 @default.
- W4313459210 hasRelatedWork W2171108821 @default.
- W4313459210 hasRelatedWork W2353074559 @default.
- W4313459210 hasRelatedWork W2358082531 @default.
- W4313459210 hasRelatedWork W2402141875 @default.
- W4313459210 hasRelatedWork W4313233093 @default.
- W4313459210 hasRelatedWork W590788508 @default.
- W4313459210 isParatext "false" @default.
- W4313459210 isRetracted "false" @default.
- W4313459210 workType "article" @default.