Very Short-Term Wind Speed Forecasting Using Spatio-Temporal Lazy Learning

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.

[1]  Donato Malerba,et al.  Wind Power Forecasting Using Time Series Cluster Analysis , 2014, Discovery Science.

[2]  U. Focken,et al.  New developments in wind energy forecasting , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[3]  Donato Malerba,et al.  Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data , 2014, ISMIS.

[4]  Gregor Giebel,et al.  Short-term Forecasting Using Advanced Physical Modelling - The Results of the Anemos Project Results from mesoscale, microscale and CFD modelling , 2006 .

[5]  Donato Malerba,et al.  An Intelligent Technique for Forecasting Spatially Correlated Time Series , 2013, AI*IA.

[6]  J. Randall Brown,et al.  Rational Arithmetic Mathematica Functions to Evaluate the One-sided One-sample K-S Cumulative Sample Distribution , 2007 .

[7]  N.D. Hatziargyriou,et al.  An Advanced Statistical Method for Wind Power Forecasting , 2007, IEEE Transactions on Power Systems.

[8]  M. Negnevitsky,et al.  Very short-term wind forecasting for Tasmanian power generation , 2006, 2006 IEEE Power Engineering Society General Meeting.

[9]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[10]  M. Negnevitsky,et al.  Short term wind power forecasting using hybrid intelligent systems , 2007, 2007 IEEE Power Engineering Society General Meeting.

[11]  João Gama,et al.  Collaborative Wind Power Forecast , 2014, ICAIS.

[12]  Albert W. L. Yao,et al.  An improved Grey-based approach for electricity demand forecasting , 2003 .

[13]  Luís Torgo,et al.  Wind speed forecasting using spatio-temporal indicators , 2012, ECAI.

[14]  Shonali Krishnaswamy,et al.  Mining data streams: a review , 2005, SGMD.