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

Result code in 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>
Alternative codes found
RIV/61989100:27740/15:86095608
Result on the web
<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>

Result language
angličtina
Original language name
Short-term hourly price forward curve prediction using Neural network and hybrid ARIMA-NN model
Original language description
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.
Czech name
—
Czech description
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Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Conference on Information and Digital Technologies, IDT 2015
ISBN
978-1-4673-7185-8
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
335-338
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
New York
Event location
Žilina
Event date
Jul 7, 2015
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000381481100052

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