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Pregled bibliografske jedinice broj: 1094709

Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System

Gonzalez-Longatt, Francisco; Acosta, Martha N.; Chamorro, Harold R.; Topić, Danijel
Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System // Proceedings of 2020 International Conference on Smart Systems and Technologies (SST) / Žagar, Drago (ur.).
Osijek: Faculty of Electrical engineering, computer science and information technology Osijek, 2020. str. 1-6 doi:10.1109/SST49455.2020.9264087 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)

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Naslov
Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System

Autori
Gonzalez-Longatt, Francisco ; Acosta, Martha N. ; Chamorro, Harold R. ; Topić, Danijel

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

Izvornik
Proceedings of 2020 International Conference on Smart Systems and Technologies (SST) / Žagar, Drago - Osijek : Faculty of Electrical engineering, computer science and information technology Osijek, 2020, 1-6

ISBN
978-1-7281-9759-3

Skup
International Conference on Smart Systems and Technologies 2020 (SST 2020)

Mjesto i datum
Osijek, Hrvatska, 14.10.2020. - 16.10.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Time series analysis , Predictive models , Forecasting , Data models , Mathematical model , Market research , Kinetic energy

Sažetak
Modern power systems are experiencing a gradual substitution of the classical synchronous generators by power electronic-based technologies ; as a consequence, there is an increased interested in estimating the total rotating inertia. This paper proposes the use of the decomposable time series model to short term forecast of the total kinetic energy (KE) of a power system. The structure of the forecasting model includes three main components: trend, a seasonal and an irregular component. As the Nordic Power System (NPS) is expected a reduction of the total kinetic energy, this paper uses a time series of KE to test the proposed approach. A cross-validation process is used in this paper, numerical results of the mean absolute percentage error indicate forecast the error in the forecasting is below 5% for predictions one hour into the future.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika



POVEZANOST RADA

Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Profili:

Avatar Url Danijel Topić (autor)

Poveznice na cjeloviti tekst rada:

doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Gonzalez-Longatt, Francisco; Acosta, Martha N.; Chamorro, Harold R.; Topić, Danijel
Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System // Proceedings of 2020 International Conference on Smart Systems and Technologies (SST) / Žagar, Drago (ur.).
Osijek: Faculty of Electrical engineering, computer science and information technology Osijek, 2020. str. 1-6 doi:10.1109/SST49455.2020.9264087 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Gonzalez-Longatt, F., Acosta, M., Chamorro, H. & Topić, D. (2020) Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System. U: Žagar, D. (ur.)Proceedings of 2020 International Conference on Smart Systems and Technologies (SST) doi:10.1109/SST49455.2020.9264087.
@article{article, author = {Gonzalez-Longatt, Francisco and Acosta, Martha N. and Chamorro, Harold R. and Topi\'{c}, Danijel}, editor = {\v{Z}agar, D.}, year = {2020}, pages = {1-6}, DOI = {10.1109/SST49455.2020.9264087}, keywords = {Time series analysis , Predictive models , Forecasting , Data models , Mathematical model , Market research , Kinetic energy}, doi = {10.1109/SST49455.2020.9264087}, isbn = {978-1-7281-9759-3}, title = {Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System}, keyword = {Time series analysis , Predictive models , Forecasting , Data models , Mathematical model , Market research , Kinetic energy}, publisher = {Faculty of Electrical engineering, computer science and information technology Osijek}, publisherplace = {Osijek, Hrvatska} }
@article{article, author = {Gonzalez-Longatt, Francisco and Acosta, Martha N. and Chamorro, Harold R. and Topi\'{c}, Danijel}, editor = {\v{Z}agar, D.}, year = {2020}, pages = {1-6}, DOI = {10.1109/SST49455.2020.9264087}, keywords = {Time series analysis , Predictive models , Forecasting , Data models , Mathematical model , Market research , Kinetic energy}, doi = {10.1109/SST49455.2020.9264087}, isbn = {978-1-7281-9759-3}, title = {Short-term Kinetic Energy Forecast using a Structural Time Series Model: Study Case of Nordic Power System}, keyword = {Time series analysis , Predictive models , Forecasting , Data models , Mathematical model , Market research , Kinetic energy}, publisher = {Faculty of Electrical engineering, computer science and information technology Osijek}, publisherplace = {Osijek, Hrvatska} }

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