Πρόβλεψη τιμών οριακής τιμής συστήματος ηλεκτρικής ενέργειας στην Ελλάδα με χρήση εξόρυξης δεδομένων και μηχανικής μάθησης
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Keywords
Data ; Electricity market ; Machine learning ; Analysis ; Python ; MySQL ; APIAbstract
This aim of this thesis is the creation of a database for the electricity market, the insertion of
data into the database, the analysis of the electricity market in Greece, and the development of
a machine learning model for forecasting electricity prices in the analyzed country. More
specifically, it involves the creation of a database using MySQL software, containing all the
necessary data collected from ENTSO-E via API and from the Investing website. Using the
Python programming language, the data were imported into the database, cleaned, and analyzed
to gain insights into the electricity market in Greece, in order to select the appropriate data for
building the forecasting model. Python was used for both the overall data management as well
as for the development of the forecasting model. The machine learning model constructed is
based on recurrent neural networks.