Forecasting electricity prices in the Czech spot market

• Název práce v češtině: Předpovídání cen elektřiny na českém spotovém trhu
• Název v anglickém jazyce: Forecasting electricity prices in the Czech spot market
• Klíčová slova: předpovídání, časové řady, ARIMA, GARCH, neurální sítě, vlnková transformace
• Klíčová slova anglicky: Time Series, Electricity, Forecasting, ARIMA, GARCH, Neural networks
• Akademický rok vypsání: 2013/2014
• Typ práce: diplomová práce
• Jazyk práce: angličtina
• Ústav: Institut ekonomických studií (23-IES)
• Vedoucí / školitel: Mgr. Michal Lebovič
• Řešitel: skrytý - zadáno vedoucím/školitelem
• Datum přihlášení: 12.02.2014
• Datum zadání: 12.02.2014
• Datum a čas obhajoby: 10.02.2016 00:00
• Místo konání obhajoby: IES
• Datum odevzdání elektronické podoby: 03.01.2016
• Datum proběhlé obhajoby: 10.02.2016
• Oponenti: Mgr. Lukáš Rečka, Ph.D.

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Předběžná náplň práce

Tato diplomová práce se zaobývá předpovídáním hodinových a denních cen elektřiny na deregulovaném českém denním trhu s elektřinou. Metody použité pro odhad a předpověď hodinových a denních cen jsou vybrány z rodiny modelů ARIMA-GARCH a neurálních sítí. Dekompozice pomocí stacionární diskrétní vlnkové transformace je použita pro denní ceny v kombinaci s ARIMA modely a neurálními sítěmi. Hodinová data jsou modelována pomocí modelů GARCH a neurálních sítí.

Výsledky předpovědí odhalují, že v případě denních cen, jednodušší modely, jako ARIMA předčí ostatní metody. Vlnková dekompozice nezlepšila přesnost předpovědí. V případě hodinových cen architektura neurální sítě Multilayer Perceptron dává lepší předpovědi než předpověd uskutečněná metodou ARIMA.

Předběžná náplň práce v anglickém jazyce

This master thesis is focused on analysis and forecasting of hourly and daily electricity price on the deregulated Czech daily electricity market. The methods used for estimating and forecasting hourly and daily prices are picked from the ARIMA-GARCH family of models and Neural Networks. For daily price data, the Redundant Haar Wavelet Transform decomposition of the time series is used in combination with ARIMA and Neural Networks models for forecasting. For hourly data, ARIMA and Neural Network models are considered.

The forecasting results of daily data indicate that simpler models such as seasonal ARIMA outperform all other methods. Also the wavelet decomposition of the daily series didn't prove useful in enhancing the forecast precision. For hourly data, the Multilayer Perceptron architecture of the neural network outperformed the ARIMA forecast.