Predictor analysis for electricity price forecasting by multiple linear regression

Name: Predictor analysis for electricity price forecasting by multiple linear regression
Author: Ülgen, Toygar, Poyrazoğlu, Göktürk

İsim	Predictor analysis for electricity price forecasting by multiple linear regression
Yazar	Ülgen, Toygar, Poyrazoğlu, Göktürk
Basım Tarihi:	2020
Basım Yeri	- IEEE
Konu	Electricity price forecasting, Multiple linear regression, Dynamic regression, Fuel price impact
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-172817019-0
Kayıt Numarası	a6b0dbe3-7f6c-4f05-b45d-2a920027cbe0
Lokasyon	Electrical & Electronics Engineering
Tarih	2020
Örnek Metin	This paper examines the multiple linear regression method on electricity price forecasting. Numerous predictors are analyzed to reduce the mean absolute percentage error. The training data includes the dates from September 2018 to September 2019 from the day-ahead electricity market in Turkey. It is proved that the lagged electricity prices such as the previous one day, one week, and lagged moving average prices play a key role in electricity price estimation. Aside from other valuable coefficients, natural gas, oil, and coal prices are tested in the forecasting model. The error rates of the fuel prices are noticeably shrunk by using multiple linear regression that generates more accurate results and crucial variables influencing hourly electricity price has determined. Different training data length is an essential part of decreasing the error proportions in the electricity price estimation. Also, it is analyzed that there is no dramatic difference regarding the error rates if it is compared to the regular regression method and dynamic regression model in the forecast of electricity prices.
DOI	10.1109/SPEEDAM48782.2020.9161866

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Predictor analysis for electricity price forecasting by multiple linear regression

Yazar Ülgen, Toygar, Poyrazoğlu, Göktürk

Basım Tarihi 2020

Basım Yeri - IEEE

Konu Electricity price forecasting, Multiple linear regression, Dynamic regression, Fuel price impact

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-172817019-0

Kayıt Numarası a6b0dbe3-7f6c-4f05-b45d-2a920027cbe0

Lokasyon Electrical & Electronics Engineering

Tarih 2020

Örnek Metin This paper examines the multiple linear regression method on electricity price forecasting. Numerous predictors are analyzed to reduce the mean absolute percentage error. The training data includes the dates from September 2018 to September 2019 from the day-ahead electricity market in Turkey. It is proved that the lagged electricity prices such as the previous one day, one week, and lagged moving average prices play a key role in electricity price estimation. Aside from other valuable coefficients, natural gas, oil, and coal prices are tested in the forecasting model. The error rates of the fuel prices are noticeably shrunk by using multiple linear regression that generates more accurate results and crucial variables influencing hourly electricity price has determined. Different training data length is an essential part of decreasing the error proportions in the electricity price estimation. Also, it is analyzed that there is no dramatic difference regarding the error rates if it is compared to the regular regression method and dynamic regression model in the forecast of electricity prices.

DOI 10.1109/SPEEDAM48782.2020.9161866