Forecasting of wind power by using a hybrid machine learning method for the Nord-Pool intraday electricity market
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This preprint has been withdrawn.
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