Matches in SemOpenAlex for { <https://semopenalex.org/work/W4285384294> ?p ?o ?g. }
Showing items 1 to 66 of
66
with 100 items per page.
- W4285384294 endingPage "108384" @default.
- W4285384294 startingPage "108384" @default.
- W4285384294 abstract "Aggregation of electrical appliances in residential households is a potent source for harnessing demand-side flexibility that can be leveraged by utilities or demand response aggregators for various transmission- and distribution-level services. However, the aggregated flexibility of these resources depends on such external factors as behavioral preferences of electricity consumers and temperature. More importantly, these external factors can be interdependent, e.g. ensuring the comfort of electricity consumers requires maintaining in-door temperatures within a certain range. This paper develops a deep learning approach for in-door temperature predictions and then integrates it with optimal load ensemble control. To improve the accuracy of deep learning, which is notorious for a lack of physical interpretability and performance guarantees, we employ the concept of physics-informed neural networks, which allows for incorporating a physical (thermal) building model. We use a real-world National Institute of Standards and Technology (NIST) data set to demonstrate the usefulness of temperature learning for such demand response application. • Physics-aware deep learning for predicting the temperature of a building ensemble. • A method for determining equivalent R and C parameters of buildings. • An integration of the deep-learning-based temperature prediction and optimal control. • Consumers are sensitive to price incentives, but not at the expense of their comfort." @default.
- W4285384294 created "2022-07-14" @default.
- W4285384294 creator A5027121765 @default.
- W4285384294 creator A5036924311 @default.
- W4285384294 creator A5082941890 @default.
- W4285384294 date "2022-10-01" @default.
- W4285384294 modified "2023-10-13" @default.
- W4285384294 title "Learning indoor temperature predictions for optimal load ensemble control" @default.
- W4285384294 cites W2034259497 @default.
- W4285384294 cites W2049425685 @default.
- W4285384294 cites W2065001413 @default.
- W4285384294 cites W2108152153 @default.
- W4285384294 cites W2132749134 @default.
- W4285384294 cites W2211524011 @default.
- W4285384294 cites W2307546884 @default.
- W4285384294 cites W2559995853 @default.
- W4285384294 cites W2899283552 @default.
- W4285384294 cites W2964266044 @default.
- W4285384294 cites W2970042010 @default.
- W4285384294 cites W2970556297 @default.
- W4285384294 cites W3022573594 @default.
- W4285384294 cites W3098600672 @default.
- W4285384294 doi "https://doi.org/10.1016/j.epsr.2022.108384" @default.
- W4285384294 hasPublicationYear "2022" @default.
- W4285384294 type Work @default.
- W4285384294 citedByCount "2" @default.
- W4285384294 countsByYear W42853842942023 @default.
- W4285384294 crossrefType "journal-article" @default.
- W4285384294 hasAuthorship W4285384294A5027121765 @default.
- W4285384294 hasAuthorship W4285384294A5036924311 @default.
- W4285384294 hasAuthorship W4285384294A5082941890 @default.
- W4285384294 hasConcept C127413603 @default.
- W4285384294 hasConcept C133731056 @default.
- W4285384294 hasConcept C154945302 @default.
- W4285384294 hasConcept C2775924081 @default.
- W4285384294 hasConcept C39432304 @default.
- W4285384294 hasConcept C41008148 @default.
- W4285384294 hasConcept C47446073 @default.
- W4285384294 hasConcept C536315585 @default.
- W4285384294 hasConceptScore W4285384294C127413603 @default.
- W4285384294 hasConceptScore W4285384294C133731056 @default.
- W4285384294 hasConceptScore W4285384294C154945302 @default.
- W4285384294 hasConceptScore W4285384294C2775924081 @default.
- W4285384294 hasConceptScore W4285384294C39432304 @default.
- W4285384294 hasConceptScore W4285384294C41008148 @default.
- W4285384294 hasConceptScore W4285384294C47446073 @default.
- W4285384294 hasConceptScore W4285384294C536315585 @default.
- W4285384294 hasLocation W42853842941 @default.
- W4285384294 hasOpenAccess W4285384294 @default.
- W4285384294 hasPrimaryLocation W42853842941 @default.
- W4285384294 hasRelatedWork W1596801655 @default.
- W4285384294 hasRelatedWork W2130043461 @default.
- W4285384294 hasRelatedWork W2350741829 @default.
- W4285384294 hasRelatedWork W2358668433 @default.
- W4285384294 hasRelatedWork W2376932109 @default.
- W4285384294 hasRelatedWork W2382290278 @default.
- W4285384294 hasRelatedWork W2390279801 @default.
- W4285384294 hasRelatedWork W2748952813 @default.
- W4285384294 hasRelatedWork W2899084033 @default.
- W4285384294 hasRelatedWork W2530322880 @default.
- W4285384294 hasVolume "211" @default.
- W4285384294 isParatext "false" @default.
- W4285384294 isRetracted "false" @default.
- W4285384294 workType "article" @default.