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Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings
Potočnik, Primož (Avtor), Vidrih, Boris (Avtor), Kitanovski, Andrej (Avtor), Govekar, Edvard (Avtor)

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URLURL - Izvorni URL, za dostop obiščite https://link.springer.com/article/10.1007%2Fs12273-019-0548-y Povezava se odpre v novem oknu

Izvleček
In this paper, the possibilities of developing machine learning based data-driven models for the short-term prediction of indoor temperature within prediction horizons ranging from 1 hour up to 12 hours are systematically investigated. The study was based on a TRNSYS emulation of a residential building heated by a heat pump, combined with measured weather data for a typical winter season in Ljubljana, Slovenia. Autoregressive models with exogenous inputs (ARX), neural network models (NN), and extreme learning machine models (ELM) are considered. The results confirm the finding that nonlinear models, particularly the NN model trained by regularization, consistently outperform linear models in both fitting and generalization performance, so they are the recommended choice as predictive models. The availability of future weather data considerably improved the predictive performance of all the tested models. Besides data about the future outdoor temperature, also data about future expected solar radiation significantly improve predictions of temperature in buildings. The linear models required embedding dimensions of 24 hours for accurate predictions, whereas the nonlinear models were not very sensitive to the use of past data. Nonlinear models required about three months of training data to reach good predictive performance, whereas the linear models converged to accurate predictions within six weeks. The RMSE prediction errors, averaged over all the data sets and all the prediction horizons, are within the range between 0.155 °C for the linear ARX model (in the case of no future available weather data), and 0.065 °C for the neural network model (in the case of available future weather data).

Jezik:Angleški jezik
Ključne besede:predictive models, neural networks, ARX model, extreme learning machines, residential buildings, indoor temperature
Vrsta gradiva:Članek v reviji (dk_c)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FS - Fakulteta za strojništvo
Leto izida:2019
Št. strani:str. 1-17
UDK:681.5:628.852(045)
ISSN pri članku:1996-3599
DOI:10.1007/s12273-019-0548-y Povezava se odpre v novem oknu
COBISS.SI-ID:16588315 Povezava se odpre v novem oknu
Število ogledov:220
Število prenosov:137
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
 
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Gradivo je financirano iz projekta

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Številka projekta:P2-0241
Naslov:Sinergetika kompleksnih sistemov in procesov

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:prediktivni modeli, nevronske mreže, ARX model, ELM model, stanovanjske zgradbe, notranja temperatura

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