Preprints
https://doi.org/10.5194/wes-2023-48
https://doi.org/10.5194/wes-2023-48
13 Jun 2023
 | 13 Jun 2023
Status: this preprint has been withdrawn by the authors.

Forecasting of wind power by using a hybrid machine learning method for the Nord-Pool intraday electricity market

Atilla Altintas, Lars Davidson, and Ola Carlson

Abstract. The interest in trading intraday markets has been increasing due to the growth of renewable intermittent energy production. With the growing renewable energy capacity, which mostly comes from wind energy, the intraday market volume has been continuously increasing every year. In Europe, countries work with different lead times ranging from 5 to 90 minutes and trading blocks of 15 minutes. Several countries, including Sweden, use 15-minute trading blocks with 60 minutes lead time. Market participants use the intraday market to optimize their position after the day-ahead market closes. Since new methods become available, such as better forecasts on short-term renewable energy power output and demand, the intraday market has become more important for energy traders in order to maximize their profit. The primary objective of this study is to enhance the intraday forecasting of wind power by improving the forecasting methods using machine learning. A hybrid approach that combines a mode decomposition method, Empirical Mode Decomposition (EMD), with Support Vector Regression (SVR), is used. In addition, the forecasting with the SVR method is improved by applying a cross-validation method that tunes the parameters used. The study utilized three months (92 days) of wind turbine power data from 21 June 2017 to 20 September 2017. 80 % of the data was used for training, and the remaining data were used for predictions. The results showed that combining SVR with a hybrid method that incorporates EMD predictions can lead to higher prediction accuracy. Furthermore, our results stress that parameter-tuning algorithms can improve machine-learning methods. We believe that the methods proposed in this study will be beneficial for the planning of dispatchable energy generation and pricing for the intraday electricity market.

This preprint has been withdrawn.

Atilla Altintas, Lars Davidson, and Ola Carlson

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-48', Anonymous Referee #1, 21 Jun 2023
  • RC2: 'Comment on wes-2023-48', Anonymous Referee #2, 26 Jul 2023
  • EC1: 'Comment on wes-2023-48', Nikolay Dimitrov, 08 Sep 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-48', Anonymous Referee #1, 21 Jun 2023
  • RC2: 'Comment on wes-2023-48', Anonymous Referee #2, 26 Jul 2023
  • EC1: 'Comment on wes-2023-48', Nikolay Dimitrov, 08 Sep 2023
Atilla Altintas, Lars Davidson, and Ola Carlson
Atilla Altintas, Lars Davidson, and Ola Carlson

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Latest update: 29 Feb 2024
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This preprint has been withdrawn.

Short summary
This study improves wind power forecasting accuracy with machine learning. A hybrid approach of Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR), with a hyperparameter tuning, leads to higher accuracy. The wind turbine power data used and found that EMD-based approach is better than SVR. Applying parameter tuning showed significant improvement in wind power forecasting. The study's findings are important for energy traders looking to maximize profits in the intraday market.
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