Naslov
Static and adaptive models in daily natural gas consumption forecasting

Autori
Potočnik, Primož ; Soldo, Božidar ; Šimunović, Goran ; Šarić, Tomislav ; Govekar, Edvard

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

Izvornik
12. skup o prirodnom plinu, toplini i vodi i 5. međunarodni skup o prirodnom plinu, toplini i vodi : zbornik radova = 12th Natural Gas, Heat and Water Conference and 5th International Natural Gas, Heat and Water Conference : proceedings / Raos, Pero - Slavonski Brod : Strojarski fakultet Sveučilišta u Slavonskom Brodu, 2014, 155-162

Skup
Skup o prirodnom plinu, toplini i vodi (12 ; 2014) ; Međunarodni skup o prirodnom plinu, toplini i vodi (5 ; 2014)

Mjesto i datum
Osijek, Hrvatska, 24.09.2014. - 26.09.2014

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
daily natural gas demand ; adaptive forecasting models ; linear forecasting models ; nonlinear forecasting models.

Sažetak
Performance of static and adaptive models for natural gas load forecasting has been investigated in this paper. The study is based on two sets of data, i.e. natural gas consumption data for an individual model house, and natural gas consumption data for a local distribution company. Several different forecasting models including linear models, neural network models, and support vector regression models, were constructed for the one day ahead forecasting of natural gas demand. All these models were examined in their static versions, and in adaptive versions. A cross- validation approach was applied in order to estimate the generalization performance of the examined forecasting models. Compared to the static model performance, the results confirmed the significantly improved forecasting performance of adaptive models in the case of the local distribution company, whereas, as was expected, the forecasts made in the case of the individual house were not improved by the adaptive models, due to its stationary heating regime. The results also revealed that nonlinear models do not outperform linear models in terms of generalization performance. In summary, if the relevant inputs are properly selected, adaptive linear models are recommended for use as basis in online application for daily natural gas consumption forecasting.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

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