Deep Belief Networks for Electricity Price Forecasting
Journal
ICIST 2018 Proc.
Date Issued
2018
Author(s)
Dedinec, Aleksandar
Abstract
In this paper, one of the aspects of the smart grids
is analyzed. This aspect includes the utilization of the large
amount of available digital information for creating smart
models for planning and forecasting. The latest and new
achievements in the field of machine learning are used for
that purpose. Specifically, models based on deep belief
networks are developed within this paper and it is examined
whether these models may be applied for electricity price
forecasting. For that purpose, the hourly data of the prices of
the power exchanges in the region of Southeast Europe are
used. The obtained results present the advantages of the
developed models based on deep belief networks, compared
to the traditional neural networks, when applied to electricity
price forecasting. To this end, the mean average percent
error of the deep belief network model is less than the
minimum error of the traditional neural network model in
each of the analyzed datasets.
is analyzed. This aspect includes the utilization of the large
amount of available digital information for creating smart
models for planning and forecasting. The latest and new
achievements in the field of machine learning are used for
that purpose. Specifically, models based on deep belief
networks are developed within this paper and it is examined
whether these models may be applied for electricity price
forecasting. For that purpose, the hourly data of the prices of
the power exchanges in the region of Southeast Europe are
used. The obtained results present the advantages of the
developed models based on deep belief networks, compared
to the traditional neural networks, when applied to electricity
price forecasting. To this end, the mean average percent
error of the deep belief network model is less than the
minimum error of the traditional neural network model in
each of the analyzed datasets.
Subjects
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