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Pregled bibliografske jedinice broj: 1131801

Prediction of User-defined Room Temperature Setpoints by LSTM Neural Networks


Perić, Nikica; Novak, Hrvoje; Rukavina, Filip; Lešić, Vinko
Prediction of User-defined Room Temperature Setpoints by LSTM Neural Networks // Proceedings of the 23rd International Conference on Process Control (PC) / Paulen, R. ; Fikar, M. (ur.).
Vysoké Tatry: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 169-174 doi:10.1109/PC52310.2021.9447534 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
Prediction of User-defined Room Temperature Setpoints by LSTM Neural Networks

Autori
Perić, Nikica ; Novak, Hrvoje ; Rukavina, Filip ; Lešić, Vinko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 23rd International Conference on Process Control (PC) / Paulen, R. ; Fikar, M. - Vysoké Tatry : Institute of Electrical and Electronics Engineers (IEEE), 2021, 169-174

ISBN
978-1-6654-4791-1

Skup
23rd International Conference on Process Control

Mjesto i datum
Vysoké Tatry, Slovačka, 01.06.2021. - 04.06.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
user behavior modelling ; room temperature reference prediction ; deep learning ; LSTM ; neural networks

Sažetak
Buildings are one of the largest consumers of energy in the world and as such an opportunity for significant energy savings. Modelling of user behavior is a prerequisite for predicting the building energy consumption that offers a possibility to adapt the building operation to best suit the user requirements while achieving minimal energy consumption through room air conditioning. In this paper, a deep learning model for prediction of an individual room temperature setpoint provided by the user is presented. Model learning was conducted on the available dataset containing external weather and internal climate measurements. External weather conditions consist of air temperature, relative humidity and direct, diffuse and global solar irradiation. The internal conditions refer to four offices for which the air temperature, the mode of operation of the fan coil controller and the temperature setpoint are available. The model was built using a neural network with Long Short- Term Memory. The highest accuracy was achieved by the model in which the variables on the old setpoint, system working, day of the week and relative humidity were used. Presented results show high model accuracy, applicable for optimization of real time building operation.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
HRZZ-UIP-2020-02-9636 - Distribuirano upravljanje za dinamičko gospodarenje energijom u složenim sustavima naprednih gradova (DECIDE) (Lešić, Vinko, HRZZ - 2020-02) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Vinko Lešić (autor)

Avatar Url Hrvoje Novak (autor)

Avatar Url Nikica Perić (autor)

Avatar Url Filip Rukavina (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi

Citiraj ovu publikaciju:

Perić, Nikica; Novak, Hrvoje; Rukavina, Filip; Lešić, Vinko
Prediction of User-defined Room Temperature Setpoints by LSTM Neural Networks // Proceedings of the 23rd International Conference on Process Control (PC) / Paulen, R. ; Fikar, M. (ur.).
Vysoké Tatry: Institute of Electrical and Electronics Engineers (IEEE), 2021. str. 169-174 doi:10.1109/PC52310.2021.9447534 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Perić, N., Novak, H., Rukavina, F. & Lešić, V. (2021) Prediction of User-defined Room Temperature Setpoints by LSTM Neural Networks. U: Paulen, R. & Fikar, M. (ur.)Proceedings of the 23rd International Conference on Process Control (PC) doi:10.1109/PC52310.2021.9447534.
@article{article, author = {Peri\'{c}, Nikica and Novak, Hrvoje and Rukavina, Filip and Le\v{s}i\'{c}, Vinko}, year = {2021}, pages = {169-174}, DOI = {10.1109/PC52310.2021.9447534}, keywords = {user behavior modelling, room temperature reference prediction, deep learning, LSTM, neural networks}, doi = {10.1109/PC52310.2021.9447534}, isbn = {978-1-6654-4791-1}, title = {Prediction of User-defined Room Temperature Setpoints by LSTM Neural Networks}, keyword = {user behavior modelling, room temperature reference prediction, deep learning, LSTM, neural networks}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Vysok\'{e} Tatry, Slova\v{c}ka} }
@article{article, author = {Peri\'{c}, Nikica and Novak, Hrvoje and Rukavina, Filip and Le\v{s}i\'{c}, Vinko}, year = {2021}, pages = {169-174}, DOI = {10.1109/PC52310.2021.9447534}, keywords = {user behavior modelling, room temperature reference prediction, deep learning, LSTM, neural networks}, doi = {10.1109/PC52310.2021.9447534}, isbn = {978-1-6654-4791-1}, title = {Prediction of User-defined Room Temperature Setpoints by LSTM Neural Networks}, keyword = {user behavior modelling, room temperature reference prediction, deep learning, LSTM, neural networks}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, publisherplace = {Vysok\'{e} Tatry, Slova\v{c}ka} }

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