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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/22/7587 |
| _version_ | 1797510519172628480 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:32:35Z |
| format | Article |
| id | doaj.art-7d7557f68a3d4bea94148882bd3aaa2a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T05:32:35Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7d7557f68a3d4bea94148882bd3aaa2a2023-11-22T23:10:28ZengMDPI AGEnergies1996-10732021-11-011422758710.3390/en14227587Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price ForecastsConor Lynch0Christian O’Leary1Preetham Govind Kolar Sundareshan2Yavuz Akin3Nimbus Research Centre, Munster Technological University, T12 Y275 Cork, IrelandNimbus Research Centre, Munster Technological University, T12 Y275 Cork, IrelandDepartment of Computer Science, Munster Technological University, T12 P928 Cork, IrelandCampus Georges Charpak Provence, École des Mines de Saint-Étienne, 880 Route de Mimet, 13120 Gardanne, FranceIn 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.https://www.mdpi.com/1996-1073/14/22/7587gradient boostingSVMelectricity price forecastingmachine learning |
| spellingShingle | 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 Energies gradient boosting SVM electricity price forecasting machine learning |
| title | 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_short | 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 |
| topic | gradient boosting SVM electricity price forecasting machine learning |
| url | https://www.mdpi.com/1996-1073/14/22/7587 |
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