Czasopismo
2018
|
Vol. 98, nr 3
|
267--273
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
Accurate electricity price forecasting is of great importance for risk-analysis and decision-making in the electricity market. However, due to the characteristics of randomness and non-linearity associated with the electricity price series, it is difficult to build a precise forecasting model. If the electricity market price can be predicted properly, the generation companies and the load service entities as the main market participating entities can reduce their risks and further maximize their outcomes. In this work, adaptive longterm electricity price forecasting modelling using Monte Carlo simulation is proposed. The applicability of the prediction performance of the method is demonstrated for the case of electricity and oil price prediction, for vaious forecasting periods. Oil price prediction is an external factor for electricity price forecasting and is becoming very important in power systems running on oil derivatives. The proposed method could be useful for long term studies, evaluating the risk for financing since good electricity price forecast feeds into developing cost effective risk management plans for the participating companies in the electricity market and thus will help attract appropriate financing.
Czasopismo
Rocznik
Tom
Strony
267--273
Opis fizyczny
Bibliogr. 27 poz., tab., wykr.
Twórcy
autor
- Cyprus Energy Regulatory Authority, P.O. Box 24936, 1305 Nicosia, Cyprus, andreas.poullikkas@cut.ac.cy
Bibliografia
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- [15] H. Takeda, Y. Tamura, S. Sato, Using the ensemble Kalman filter for electricity load forecasting and analysis, Energy 104 (2016) 184–198. doi:10.1016/j.energy.2016.03.070.
- [16] I. P. Panapakidis, A. S. Dagoumas, Day-ahead electricity price forecasting via the application of artificial neural network based models, Applied Energy 172 (2016) 132–151. doi:10.1016/j.apenergy.2016.03.089.
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- [24] T. Xiong, Y. Bao, Z. Hu, Interval forecasting of electricity demand: A novel bivariate EMD-based support vector regression modeling framework, International Journal of Electrical Power and Energy Systems 63 (2014) 353–362. doi:10.1016/j.ijepes.2014.06.010.
- [25] D. Wang, H. Luo, O. Grunder, Y. Lin, H. Guo, Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm, Applied Energy 190 (2017) 390–407. doi:10.1016/j.apenergy.2016.12.134.
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- [27] U.S. Energy Information Administration, www.eia.gov (2017). URL https://www.eia.gov/electricity/wholesale/
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-7c5be400-f140-4c03-838b-94146605894e