Document Type
Article
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Disciplines
Computer Sciences | Physical Sciences and Mathematics
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.
Recommended Citation
Lynch, C.; O’Leary, C.; Sundareshan, P.G.K.; Akin, Y. Experimental Analysis of GBM to Expand the Time Horizon of Irish Electricity Price Forecasts. Energies 2021, 14, 7587. https://doi.org/10.3390/en14227587
Publication Details
Energies 2021, 14(22). © 2021 by the authors.