Long-term prediction of underground gas storage user gas flow nominations

• Autorzy: Kurek Teresa , Wojdan Konrad , Świrski Konrad
• Języki publikacji: EN

Abstrakty

EN

Many companies operating on the natural gas market use natural gas storage to balance production and transport capacities with major variations in gas demand. This paper presents an approach to predicting users’ gas flow nomination in underground gas storage by different users. A one-year prediction horizon is considered with weekly data resolution. Basic models show that whereas for the great majority of users we can predict nomination based only on weather data and technical parameters, for some users additional macro-economic data significantly improved prediction accuracy. Various modeling techniques such as linear regression, autoregressive exogenous model and Artificial Neural Network were used to develop prediction models. Results show that for most users an Artificial Neural Network provides optimal accuracy, indicating the non-linearity of the relationship between input and output variables. The models developed are intended to be used as support for facility operation decisions and gas storage product portfolio modifications.

Słowa kluczowe

EN

underground gas storage; long term forecasting; artificial neural networks; prediction models; torque controlled induction motor drive

PL

magazynowanie gazu ziemnego; prognozowanie długoterminowe; sieci neuronowe sztuczne; modele prognostyczne; prognozowanie popytu

• Wydawca: Institute of Heat Engineering, Warsaw University of Technology
• Czasopismo: Journal of Power Technologies
• Rocznik: 2019
• Tom: Vol. 99, nr 4
• Strony: 272--280
• Opis fizyczny: Bibliogr. 17 poz., rys., tab., wykr.

Twórcy

autor

Kurek Teresa

• Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 21/25, 00-665 Warsaw, Poland

autor

Wojdan Konrad

• Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 21/25, 00-665 Warsaw, Poland

autor

Świrski Konrad

• Institute of Heat Engineering, Faculty of Power and Aeronautical Engineering, Warsaw University of Technology, Nowowiejska 21/25, 00-665 Warsaw, Poland

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).