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Modeliranje časovne vrste neto pozicije električne energije za Nemčijo
ID Demšar, Eva (Author), ID Kastelec, Damijana (Mentor) More about this mentor... This link opens in a new window

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MD5: D5A247B4C4C0259FCDB49AD64B264D6E
PID: 20.500.12556/rul/67b5328f-4389-4701-96ea-d1a251689a1b

Abstract
V nalogi je analizirana časovna vrsta urne razlike med izvozom in uvozom električne energije za Nemčijo. Razliko med izvozom in uvozom električne energije z drugo besedo imenujemo neto pozicija električne energije in je eden izmed pomembnejših pojmov mednarodnega trgovanja z električno energijo. Neto pozicija sledi proizvodnji in porabi električne energije, ki se spreminjata po urah, dnevih in sezonah. Predmet raziskovanja je kratkoročna napoved neto pozicije. Analizirani podatki se navezujejo na urne vrednosti obdobja med 1. 6. 2013 in 31. 5. 2014. Narava časovne vrste urne neto pozicije narekuje uporabo sezonskega integriranega avtoregresijskega modela in modela drsečih sredin (SARIMA). V nalogi so uporabljeni različni časovni intervali urnih mesečnih podatkov, ki jih obravnavamo v namen modeliranja. Uporabljena je tudi serija simulacij za namen ovrednotenja modelov ter navzkrižno preverjanje za primerjavo točnosti napovedi po izbranih SARIMA modelih.

Language:Slovenian
Keywords:analiza časovnih vrst, neto pozicija električne energije, napovedovanje časovnih vrst, sezonski ARIMA
Work type:Master's thesis/paper
Organization:FE - Faculty of Electrical Engineering
Year:2016
PID:20.500.12556/RUL-87015 This link opens in a new window
Publication date in RUL:14.11.2016
Views:3678
Downloads:846
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Secondary language

Language:English
Title:Time series modelling of net position of electrical energy for German
Abstract:
The hourly time series of differences between export and import of electrical energy for Germany have been studied. Difference between export and import is called net position, and it is one of the most important terms in the world trade of electrical energy. Net position follows the production and consumption of electrical energy, which vary according to hours, days and seasons. The objective of our research is short term forecast of net position. The analyzed data consists of the hourly values of the net position for the period between 1.6.2013 and 31.5.2014. The time series of hourly net position was modelled as a seasonal autoregressive integrated moving average process (SARIMA). Various one-month time intervals of the data were considered as training sets. The time series simulation was used for evaluation of the models and cross validation for accuracy of the forecasts of different models was analyzed and compared.

Keywords:forecasting, net position, time series anaysis, seasonal ARIMA

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