Electricity Spot Prices Forecasting Based on Ensemble Learning

Efficient modeling and forecasting of electricity prices are essential in today’s competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for fo...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nadeela Bibi, Ismail Shah, Abdelaziz Alsubie, Sajid Ali, Showkat Ahmad Lone
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Electricity prices forecasting semi-parametric IPEX autoregressive Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a0f2d22fea264800a07d1f9a12a2c3c2
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:a0f2d22fea264800a07d1f9a12a2c3c2
record_format	dspace
spelling	oai:doaj.org-article:a0f2d22fea264800a07d1f9a12a2c3c22021-11-18T00:08:29ZElectricity Spot Prices Forecasting Based on Ensemble Learning2169-353610.1109/ACCESS.2021.3126545https://doaj.org/article/a0f2d22fea264800a07d1f9a12a2c3c22021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9606707/https://doaj.org/toc/2169-3536Efficient modeling and forecasting of electricity prices are essential in today’s competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for forecasting short-term electricity spot prices in the Italian electricity market (IPEX). To this end, the price time series is divided into deterministic and stochastic components. The deterministic component that includes long-term trends, annual and weekly seasonality, and bank holidays, is estimated using semi-parametric techniques. On the other hand, the stochastic component considers the short-term dynamics of the price series and is estimated by time series and various machine learning algorithms. Based on three standard accuracy measures, the results indicate that the ensemble-based model outperforms the others, while the random forest and ARMA are highly competitive.Nadeela BibiIsmail ShahAbdelaziz AlsubieSajid AliShowkat Ahmad LoneIEEEarticleElectricity pricesforecastingsemi-parametricIPEXautoregressiveElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150984-150992 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electricity prices forecasting semi-parametric IPEX autoregressive Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Electricity prices forecasting semi-parametric IPEX autoregressive Electrical engineering. Electronics. Nuclear engineering TK1-9971 Nadeela Bibi Ismail Shah Abdelaziz Alsubie Sajid Ali Showkat Ahmad Lone Electricity Spot Prices Forecasting Based on Ensemble Learning
description	Efficient modeling and forecasting of electricity prices are essential in today’s competitive electricity markets. However, price forecasting is not easy due to the specific features of the electricity price series. This study examines the performance of an ensemble-based technique for forecasting short-term electricity spot prices in the Italian electricity market (IPEX). To this end, the price time series is divided into deterministic and stochastic components. The deterministic component that includes long-term trends, annual and weekly seasonality, and bank holidays, is estimated using semi-parametric techniques. On the other hand, the stochastic component considers the short-term dynamics of the price series and is estimated by time series and various machine learning algorithms. Based on three standard accuracy measures, the results indicate that the ensemble-based model outperforms the others, while the random forest and ARMA are highly competitive.
format	article
author	Nadeela Bibi Ismail Shah Abdelaziz Alsubie Sajid Ali Showkat Ahmad Lone
author_facet	Nadeela Bibi Ismail Shah Abdelaziz Alsubie Sajid Ali Showkat Ahmad Lone
author_sort	Nadeela Bibi
title	Electricity Spot Prices Forecasting Based on Ensemble Learning
title_short	Electricity Spot Prices Forecasting Based on Ensemble Learning
title_full	Electricity Spot Prices Forecasting Based on Ensemble Learning
title_fullStr	Electricity Spot Prices Forecasting Based on Ensemble Learning
title_full_unstemmed	Electricity Spot Prices Forecasting Based on Ensemble Learning
title_sort	electricity spot prices forecasting based on ensemble learning
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a0f2d22fea264800a07d1f9a12a2c3c2
work_keys_str_mv	AT nadeelabibi electricityspotpricesforecastingbasedonensemblelearning AT ismailshah electricityspotpricesforecastingbasedonensemblelearning AT abdelazizalsubie electricityspotpricesforecastingbasedonensemblelearning AT sajidali electricityspotpricesforecastingbasedonensemblelearning AT showkatahmadlone electricityspotpricesforecastingbasedonensemblelearning
_version_	1718425241687949312

Electricity Spot Prices Forecasting Based on Ensemble Learning

Ejemplares similares