HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Conference papers

Wind Energy Forecasting at Different Time Horizons with Individual and Global Models

Abstract : In this work two different machine learning approaches have been studied to predict wind power for different time horizons: individual and global models. The individual approach constructs a model for each horizon while the global approach obtains a single model that can be used for all horizons. Both approaches have advantages and disadvantages. Each individual model is trained with data pertaining to a single horizon, thus it can be specific for that horizon, but can use fewer data for training than the global model, which is constructed with data belonging to all horizons. Support Vector Machines have been used for constructing the individual and global models. This study has been tested on energy production data obtained from the Sotavento wind farm and meteorological data from the European Centre for Medium-Range Weather Forecasts, for a 5 × 5 grid around Sotavento. Also, given the large amount of variables involved, a feature selection algorithm (Sequential Forward Selection) has been used in order to improve the performance of the models. Experimental results show that the global model is more accurate than the individual ones, specially when feature selection is used.
Document type :
Conference papers
Complete list of metadata

Cited literature [11 references]  Display  Hide  Download

Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, June 22, 2018 - 11:45:32 AM
Last modification on : Sunday, May 31, 2020 - 6:50:03 PM
Long-term archiving on: : Tuesday, September 25, 2018 - 6:52:35 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



R. Martín-Vázquez, R. Aler, I. Galván. Wind Energy Forecasting at Different Time Horizons with Individual and Global Models. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.240-248, ⟨10.1007/978-3-319-92007-8_21⟩. ⟨hal-01821061⟩



Record views


Files downloads