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Pregled bibliografske jedinice broj: 1205601

Learning Indoor Temperature Predictions for Optimal Load Ensemble Control


Čović, Nikolina; Pandžić, Hrvoje; Dvorkin, Yury
Learning Indoor Temperature Predictions for Optimal Load Ensemble Control // Electric power systems research, 211 (2022), 108384, 6 doi:10.1016/j.epsr.2022.108384 (međunarodna recenzija, članak, znanstveni)


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Naslov
Learning Indoor Temperature Predictions for Optimal Load Ensemble Control

Autori
Čović, Nikolina ; Pandžić, Hrvoje ; Dvorkin, Yury

Izvornik
Electric power systems research (0378-7796) 211 (2022); 108384, 6

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Markov decision process Physics-informed machine learning Smart buildings

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekti:
IP-2019-04-9164 - Aktivno sudjelovanje skupine kućanstava u energetskim tržištima (ANIMATION) (Pandžić, Hrvoje, HRZZ - 2019-04) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Hrvoje Pandžić (autor)

Avatar Url Nikolina Čović (autor)

Poveznice na cjeloviti tekst rada:

doi www.sciencedirect.com

Citiraj ovu publikaciju:

Čović, Nikolina; Pandžić, Hrvoje; Dvorkin, Yury
Learning Indoor Temperature Predictions for Optimal Load Ensemble Control // Electric power systems research, 211 (2022), 108384, 6 doi:10.1016/j.epsr.2022.108384 (međunarodna recenzija, članak, znanstveni)
Čović, N., Pandžić, H. & Dvorkin, Y. (2022) Learning Indoor Temperature Predictions for Optimal Load Ensemble Control. Electric power systems research, 211, 108384, 6 doi:10.1016/j.epsr.2022.108384.
@article{article, author = {\v{C}ovi\'{c}, Nikolina and Pand\v{z}i\'{c}, Hrvoje and Dvorkin, Yury}, year = {2022}, pages = {6}, DOI = {10.1016/j.epsr.2022.108384}, chapter = {108384}, keywords = {Markov decision process Physics-informed machine learning Smart buildings}, journal = {Electric power systems research}, doi = {10.1016/j.epsr.2022.108384}, volume = {211}, issn = {0378-7796}, title = {Learning Indoor Temperature Predictions for Optimal Load Ensemble Control}, keyword = {Markov decision process Physics-informed machine learning Smart buildings}, chapternumber = {108384} }
@article{article, author = {\v{C}ovi\'{c}, Nikolina and Pand\v{z}i\'{c}, Hrvoje and Dvorkin, Yury}, year = {2022}, pages = {6}, DOI = {10.1016/j.epsr.2022.108384}, chapter = {108384}, keywords = {Markov decision process Physics-informed machine learning Smart buildings}, journal = {Electric power systems research}, doi = {10.1016/j.epsr.2022.108384}, volume = {211}, issn = {0378-7796}, title = {Learning Indoor Temperature Predictions for Optimal Load Ensemble Control}, keyword = {Markov decision process Physics-informed machine learning Smart buildings}, chapternumber = {108384} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


Citati:





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