Abstract
A wind speed forecast corresponds to an estimate of the upcoming production of a wind farm. The paper illustrates a variant of the Nearest Neighbor algorithm that yields wind speed forecasts, with a fast time resolution, for a (very) short time horizon. The proposed algorithm allows us to monitor a grid of wind farms, which collaborate by sharing information (i.e. wind speed measurements). It accounts for both spatial and temporal correlation of shared information. Experiments show that the presented algorithm is able to determine more accurate forecasts than a state-of-art statistical algorithm, namely auto. ARIMA.
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Notes
- 1.
Multi-dimensional representation of geographic space can be equally dealt.
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Acknowledgments
Authors thank Giuseppe Mumolo for his support in developing the algorithm presented. This work is carried out in partial fulfillment of the research objectives of both the Startup project “VIPOC: Virtual Power Operation Center” funded by the Italian Ministry of University and Research (MIUR) and the European project “MAESTRA - Learning from Massive, Incompletely annotated, and Structured Data (Grant number ICT-2013-612944)” funded by the European Commission.
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Appice, A., Pravilovic, S., Lanza, A., Malerba, D. (2015). Very Short-Term Wind Speed Forecasting Using Spatio-Temporal Lazy Learning. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_2
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DOI: https://doi.org/10.1007/978-3-319-24282-8_2
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