Impact of gas price on electricity price forecasting via supervised learning and random walk

Name: Impact of gas price on electricity price forecasting via supervised learning and random walk
Author: Poyrazoğlu, Göktürk

İsim	Impact of gas price on electricity price forecasting via supervised learning and random walk
Yazar	Poyrazoğlu, Göktürk
Basım Tarihi:	2019
Basım Yeri	- IEEE
Konu	Electricity price forecasting, Natural gas price, Price formation, Multiple linear regression, Interaction regression, Lagged price
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-7281-1257-2
Kayıt Numarası	f3f2a786-e144-4880-96ad-8b5833b64f63
Lokasyon	Electrical & Electronics Engineering
Tarih	2019
Örnek Metin	The electricity is a regular commodity that is being sold and bought in a highly transparent and efficient market in Turkey. The market is operated by EXIST and an hourly energy price is formed for every hour in the day-ahead market. In Sept. 2018, EXIST also found a central natural gas market in Turkey which enables a ground for all shareholders in the natural gas industry. This study examines the impact of natural gas prices formed in the market on the electricity price. Different predictors are tested to lower the mean absolute percentage error. Addition of past natural gas price into the forecasting model reduces the error from 15.85% to 14.31% when the average of the last two weeks' natural gas price is used. This may indicate that the current natural gas price affects the electricity market two weeks later. The linear regression-based machine learning model doesn't include any random process; however, the proposed Fourier transform-based random walk forecasting method in this study does. The comparison of the forecasts is discussed on the effectiveness of the estimations on the Turkish DAM prices.
DOI	10.1109/EEM.2019.8916448

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Impact of gas price on electricity price forecasting via supervised learning and random walk

Yazar Poyrazoğlu, Göktürk

Basım Tarihi 2019

Basım Yeri - IEEE

Konu Electricity price forecasting, Natural gas price, Price formation, Multiple linear regression, Interaction regression, Lagged price

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-7281-1257-2

Kayıt Numarası f3f2a786-e144-4880-96ad-8b5833b64f63

Lokasyon Electrical & Electronics Engineering

Tarih 2019

Örnek Metin The electricity is a regular commodity that is being sold and bought in a highly transparent and efficient market in Turkey. The market is operated by EXIST and an hourly energy price is formed for every hour in the day-ahead market. In Sept. 2018, EXIST also found a central natural gas market in Turkey which enables a ground for all shareholders in the natural gas industry. This study examines the impact of natural gas prices formed in the market on the electricity price. Different predictors are tested to lower the mean absolute percentage error. Addition of past natural gas price into the forecasting model reduces the error from 15.85% to 14.31% when the average of the last two weeks' natural gas price is used. This may indicate that the current natural gas price affects the electricity market two weeks later. The linear regression-based machine learning model doesn't include any random process; however, the proposed Fourier transform-based random walk forecasting method in this study does. The comparison of the forecasts is discussed on the effectiveness of the estimations on the Turkish DAM prices.

DOI 10.1109/EEM.2019.8916448