Abstract
Wind speed forecasting is particularly important for wind farms due to cost-related issues, dispatch planning, and energy markets operations. This paper presents a multi-target learning method, in order to model historical wind speed data and yield accurate forecasts of the wind speed on the day-ahead (24 h) horizon. The proposed method is based on the analysis of historical data, which are represented at multiple scales in both space and time. Handling multi-scale data allows us to leverage the knowledge hidden in both the spatial and temporal variability of the shared information, in order to identify spatio-temporal aided patterns that contribute to yield accurate wind speed forecasts. The viability of the presented method is evaluated by considering benchmark data. Specifically, the empirical study shows that learning multi-scale historical data allows us to determine accurate wind speed forecasts.
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Notes
- 1.
Multi-dimensional representations of geographic space can be equally dealt.
- 2.
The traditional multi-target predictive model \(f:\mathbf {X} \rightarrow \mathbf {Y}\) can be learned by neglecting information on the data variability.
- 3.
Information on the wind speed variability is included in the learning setting as a constraint to improve the predictive ability of the forecasting model learned.
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Acknowledgments
Authors thank Enrico Laboragine for his support in developing the algorithm presented and running the experiments. This work is carried out in partial fulfillment of the research objectives of ATENEO Project 2014 on “Mining of network data” and ATENEO Project 2015 on“Models and Methods to Mine Complex and Large Data” funded by the University of Bari Aldo Moro.
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Appice, A., Lanza, A., Malerba, D. (2018). Handling Multi-scale Data via Multi-target Learning for Wind Speed Forecasting. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_34
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