A local semi-supervised ensemble learning strategy for the data-driven soft sensor of the power prediction in wind power generation

Soft sensors have been widely employed to estimate the power prediction in wind power generation that are difficult to measure in real-time. However, strongly nonlinear, dynamic time-varying, and multi-rate data characteristics are major causes of poor soft sensor performance. To address this problem, a strategy of building a soft sensor model based on local semi-supervised ensemble learning of least squares support vector regression (LS-EL-LSSVR) is proposed in this paper. Firstly, to reduce the influence of nonlinearity, a localization method based on the combination of time and space criteria is proposed, which can overcome the drawback of the clustering method based on a spatial criterion. Subsequently, the self-training semi-supervised classification method is used to achieve semi-supervised clustering of unlabeled samples to improve the generalization performance of the soft sensor model. Afterward, based on the local semi-supervised clustering sample dataset, the soft sensor model is established according to the least squares support vector machine method. Moreover, a selective ensemble learning method based on the prediction accuracy of local models and an adaptive combination weight calculation method of sub-models is proposed to realize selective ensemble learning and improve the prediction accuracy. The parameters of the proposed method are finalized automatically by the particle swarm optimization technique. The proposed method is simulated by using the actual spatial dynamic wind power prediction dataset, the results demonstrate the effectiveness of the proposed method in dealing with nonlinear, dynamic time-varying, and multi-rate data regression problems in wind power generation process.