Electricity price forecasting of the South East European power exchanges
Journal
ICIST 2017 Proceedings
Date Issued
2017
Author(s)
Dedinec, Aleksandar
Abstract
The deregulation of the electricity market is a
process which is currently very hot topic in the Southeast
European region. Namely, few of the countries have their
own power exchanges from long time ago, but few of them
have just formed ones and the remaining countries need to
decide in the near future the direction they are going to
follow towards resolving this problem. With the
introduction of the power exchanges and the fact that half of
the power markets in the Southeast European region exist
less than one year, forecasting the electricity price on those
markets is becoming very attractive research area and is of
great importance. In this paper, 24-hours ahead forecasting
of the electricity price in these newly formed power
exchanges is. To this end, an artificial intelligence models,
specifically neural networks are used in this paper, which as
an input use all information that are relevant for the
corresponding power exchange price forecasting. The
results show that among the newly formed power exchanges
in the region, the price in Bulgarian power exchange is the
most unpredictable one, while, on the other hand, the price
in the Serbian power exchange is the most predictable one.
Additionally, the results present in which hours of the day
and in which days in the week the prices have the highest
variations.
process which is currently very hot topic in the Southeast
European region. Namely, few of the countries have their
own power exchanges from long time ago, but few of them
have just formed ones and the remaining countries need to
decide in the near future the direction they are going to
follow towards resolving this problem. With the
introduction of the power exchanges and the fact that half of
the power markets in the Southeast European region exist
less than one year, forecasting the electricity price on those
markets is becoming very attractive research area and is of
great importance. In this paper, 24-hours ahead forecasting
of the electricity price in these newly formed power
exchanges is. To this end, an artificial intelligence models,
specifically neural networks are used in this paper, which as
an input use all information that are relevant for the
corresponding power exchange price forecasting. The
results show that among the newly formed power exchanges
in the region, the price in Bulgarian power exchange is the
most unpredictable one, while, on the other hand, the price
in the Serbian power exchange is the most predictable one.
Additionally, the results present in which hours of the day
and in which days in the week the prices have the highest
variations.
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