Research Article
Forecasting of Day-Ahead Wind Speed/electric Power by Using a Hybrid Machine Learning Algorithm
@INPROCEEDINGS{10.1007/978-3-031-33979-0_1, author={Atilla Altıntaş and Lars Davidson and Ola Carlson}, title={Forecasting of Day-Ahead Wind Speed/electric Power by Using a Hybrid Machine Learning Algorithm}, proceedings={Sustainable Energy for Smart Cities. 4th EAI International Conference, SESC 2022, Braga, Portugal, November 16-18, 2022, Proceedings}, proceedings_a={SESC}, year={2023}, month={5}, keywords={Wind energy wind turbine Empirical Mode Decomposition (EMD) forecasting machine learning renewable energy grid integration energy market}, doi={10.1007/978-3-031-33979-0_1} }- Atilla Altıntaş
Lars Davidson
Ola Carlson
Year: 2023
Forecasting of Day-Ahead Wind Speed/electric Power by Using a Hybrid Machine Learning Algorithm
SESC
Springer
DOI: 10.1007/978-3-031-33979-0_1
Abstract
The amount of energy that has to be delivered for the following day is currently predicted by power system operators using day-ahead load forecasts. With the use of this forecast, generation resources can be committed a day in advance, some of them may require several hours’ notice to be ready to produce power the following day. In order to determine how much wind power will be available for each hour of the following day, power systems with large penetrations of wind generation rely on day-ahead predictions. The main objective of this study is to improve the day-ahead forecasting of wind power by improving the forecasting method using machine learning. A hybrid approach, which combines a mode decomposition method, Empirical Mode Decomposition (EMD), with Support Vector Regression (SVR), is used. The results suggest that using Support Vector Regression together with the hybrid method, which includes the Empirical Mode Decomposition to predictions can improve the accuracy of predictions. Higher accuracy forecasting of wind power is expected to improve the planning of dispatchable energy generation and pricing for the day-ahead power market.
