Prediction algorithm & learner selection for European day-ahead electricity prices

Name: Prediction algorithm & learner selection for European day-ahead electricity prices
Author: Ülgen, Toygar, El Sayed, Ahmad, Poyrazoğlu, Göktürk

İsim	Prediction algorithm & learner selection for European day-ahead electricity prices
Yazar	Ülgen, Toygar, El Sayed, Ahmad, Poyrazoğlu, Göktürk
Basım Tarihi:	2020-10
Basım Yeri	- IEEE
Konu	Time-series prediction methods, Electricity price, Forecasting, Classification
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-7281-6264-5
Kayıt Numarası	398f008e-7a6b-4fd7-8cd0-e360ef907625
Lokasyon	Electrical & Electronics Engineering
Tarih	2020-10
Örnek Metin	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

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Prediction algorithm & learner selection for European day-ahead electricity prices

Yazar Ülgen, Toygar, El Sayed, Ahmad, Poyrazoğlu, Göktürk

Basım Tarihi 2020-10

Basım Yeri - IEEE

Konu Time-series prediction methods, Electricity price, Forecasting, Classification

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-7281-6264-5

Kayıt Numarası 398f008e-7a6b-4fd7-8cd0-e360ef907625

Lokasyon Electrical & Electronics Engineering

Tarih 2020-10

Örnek Metin 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