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Time series wind power forecasting based on variant Gaussian Process and TLBO
Journal article   Peer reviewed

Time series wind power forecasting based on variant Gaussian Process and TLBO

Juan Yan, Kang Li, Erwei Bai, Zhile Yang and Aoife Foley
Neurocomputing (Amsterdam), Vol.189, pp.135-144
05/12/2016
DOI: 10.1016/j.neucom.2015.12.081

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Abstract

Due to the variability and stochastic nature of wind power, accurate wind power forecasting plays an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. The convergence of the forecasting results is also proved. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation in Ireland and that from a single wind farm to demonstrate the effectiveness of the proposed method.
 Gaussian Process Model consistency TLBO Wind power forecasting

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