Author
Ülgen, Toygar, El Sayed, Ahmad, Poyrazoğlu, Göktürk
Publication Date
2020-10
Publication Place
-
IEEE
Subject
Time-series prediction methods, Electricity price, Forecasting, Classification
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-1-7281-6264-5
Record ID
398f008e-7a6b-4fd7-8cd0-e360ef907625
Library Location
Electrical & Electronics Engineering
Date
2020-10
Sample Text
The prediction of day-ahead electricity prices with higher accuracy is always helpful for the market players of the power exchange. This study was intended in the first place to find out the best time series prediction method for the selected 14 European countries. The test results of four time-series methods show that the next day prices were more in line with the previous day prices in 87% of the selected countries; Later, a classification approach is followed by 33 different features of each country to answer the question of which method would be the best for the other countries, that were not studied in this paper, would be? As a result, the support vector machine algorithm results in 57% accuracy in classifying an unknown European country to determine the best prediction method. Therefore, this paper focuses now on two correlated studies to find out the best time series prediction methods and a classification approach for selected countries.
DOI
10.1109/GPECOM49333.2020.9247915
