Short-term hourly price forward curve prediction using Neural network and hybrid ARIMA-NN model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86095608" target="_blank" >RIV/61989100:27240/15:86095608 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/15:86095608

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7222993" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7222993</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/DT.2015.7222993" target="_blank" >10.1109/DT.2015.7222993</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Short-term hourly price forward curve prediction using Neural network and hybrid ARIMA-NN model

Popis výsledku v původním jazyce

Even though the electricity HPFC (Hourly Price Forward Curve) is still surprisingly under-researched the prediction of electricity prices is highly important in order to keep power plants profitable or in order to optimize the electricity purchases based on future customers demand. In this work two methods to model and predict HPFC based on neural networks will be proposed and compared to more common time series approach - specifically ARIMA model. In the first method the neural network is applied to model the price at desired time as a function of some past observations and also to capture the seasonal character of the data. The second method uses hybrid model which consist of an ARIMA model combined with neural network. The ARIMA is used to capture linear patterns in the data. Then the neural network is used to model remaining non-linear residuals. In this case the whole process is done on deseasonalized data set. Both methods provide more accurate predictions than standard time series approach (in this case ARIMA model) and results clearly state that the neural network pproach is a valid alternative for forecasting (not just) economic time series.

Název v anglickém jazyce

Short-term hourly price forward curve prediction using Neural network and hybrid ARIMA-NN model

Popis výsledku anglicky

Even though the electricity HPFC (Hourly Price Forward Curve) is still surprisingly under-researched the prediction of electricity prices is highly important in order to keep power plants profitable or in order to optimize the electricity purchases based on future customers demand. In this work two methods to model and predict HPFC based on neural networks will be proposed and compared to more common time series approach - specifically ARIMA model. In the first method the neural network is applied to model the price at desired time as a function of some past observations and also to capture the seasonal character of the data. The second method uses hybrid model which consist of an ARIMA model combined with neural network. The ARIMA is used to capture linear patterns in the data. Then the neural network is used to model remaining non-linear residuals. In this case the whole process is done on deseasonalized data set. Both methods provide more accurate predictions than standard time series approach (in this case ARIMA model) and results clearly state that the neural network pproach is a valid alternative for forecasting (not just) economic time series.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

International Conference on Information and Digital Technologies, IDT 2015

ISBN

978-1-4673-7185-8

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

335-338

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Žilina

Datum konání akce

7. 7. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000381481100052