Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts

In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electrici...

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Autores principales:	Conor Lynch, Christian O’Leary, Preetham Govind Kolar Sundareshan, Yavuz Akin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gradient boosting SVM electricity price forecasting machine learning Technology T
Acceso en línea:	https://doaj.org/article/7d7557f68a3d4bea94148882bd3aaa2a
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spelling	oai:doaj.org-article:7d7557f68a3d4bea94148882bd3aaa2a2021-11-25T17:26:57ZExperimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts10.3390/en142275871996-1073https://doaj.org/article/7d7557f68a3d4bea94148882bd3aaa2a2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7587https://doaj.org/toc/1996-1073In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan electricity utilization from the grid to be as cost effectively as possible. Extreme Gradient Boosting Machine (XGBM) models achieved a Mean Absolute Error (MAE) of 9.93 for data from 30 September 2018 to 12 December 2019 which is an 11.4% improvement on the avant-garde. LGBM models achieve a MAE score 9.58 on more recent data: the full year of 2020.Conor LynchChristian O’LearyPreetham Govind Kolar SundareshanYavuz AkinMDPI AGarticlegradient boostingSVMelectricity price forecastingmachine learningTechnologyTENEnergies, Vol 14, Iss 7587, p 7587 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gradient boosting SVM electricity price forecasting machine learning Technology T
spellingShingle	gradient boosting SVM electricity price forecasting machine learning Technology T Conor Lynch Christian O’Leary Preetham Govind Kolar Sundareshan Yavuz Akin Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts
description	In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models are applied to data streams from the Irish electricity market and achieve favorable results, relative to the current state-of-the-art. Presently, electricity prices are published 10 h in advance of the trade day of interest. Using the forecasting methodology outlined in this paper, an estimation of these prices can be made available one day in advance of the official price publication, thus extending the time available to plan electricity utilization from the grid to be as cost effectively as possible. Extreme Gradient Boosting Machine (XGBM) models achieved a Mean Absolute Error (MAE) of 9.93 for data from 30 September 2018 to 12 December 2019 which is an 11.4% improvement on the avant-garde. LGBM models achieve a MAE score 9.58 on more recent data: the full year of 2020.
format	article
author	Conor Lynch Christian O’Leary Preetham Govind Kolar Sundareshan Yavuz Akin
author_facet	Conor Lynch Christian O’Leary Preetham Govind Kolar Sundareshan Yavuz Akin
author_sort	Conor Lynch
title	Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts
title_short	Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts
title_full	Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts
title_fullStr	Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts
title_full_unstemmed	Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts
title_sort	experimental analysis of gbm to expand the time horizon of irish electricity price forecasts
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7d7557f68a3d4bea94148882bd3aaa2a
work_keys_str_mv	AT conorlynch experimentalanalysisofgbmtoexpandthetimehorizonofirishelectricitypriceforecasts AT christianoleary experimentalanalysisofgbmtoexpandthetimehorizonofirishelectricitypriceforecasts AT preethamgovindkolarsundareshan experimentalanalysisofgbmtoexpandthetimehorizonofirishelectricitypriceforecasts AT yavuzakin experimentalanalysisofgbmtoexpandthetimehorizonofirishelectricitypriceforecasts
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Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts

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