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

Conor Lynch, Christian O’Leary, Preetham Govind Kolar Sundareshan and Yavuz Akin
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Conor Lynch: Nimbus Research Centre, Munster Technological University, T12 Y275 Cork, Ireland
Christian O’Leary: Nimbus Research Centre, Munster Technological University, T12 Y275 Cork, Ireland
Preetham Govind Kolar Sundareshan: Department of Computer Science, Munster Technological University, T12 P928 Cork, Ireland
Yavuz Akin: Campus Georges Charpak Provence, École des Mines de Saint-Étienne, 880 Route de Mimet, 13120 Gardanne, France

Energies, 2021, vol. 14, issue 22, 1-11

Abstract: 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.

Keywords: gradient boosting; SVM; electricity price forecasting; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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