Abstract
A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.
Highlights
The uncertainty of wind energy imposes major challenges on power system operation and planning, such as power system security assessment and reserve management [1]
Owing to the improvement of the forecasting accuracy for highdimensional and large-scale wind turbine data, we propose an optimized random forest method which consists of a dimension reduction procedure and the weighted voting process for the short-term wind power forecasting (WPF)
To improve the prediction accuracy of the random forest model, we present a Max Relevance-Min Redundancy (MRMR) index by conducting Pearson’s correlation coefficient to reduce the number of dimensions
Summary
The uncertainty of wind energy imposes major challenges on power system operation and planning, such as power system security assessment and reserve management [1]. A reliable wind power forecasting plays an important role in preventing damage to wind turbines and maintaining the stability and security of the power system. Numerous forecasting models which are generally classified into four categories have been proposed for short-term wind power forecasting. (1) Physical-based method includes spatial and temporal factors in a full fluid-dynamics model of the atmosphere [2]. (2) Statistical-based method characterizes the history data to yield precise performance for short-term forecasting tasks. The dynamical ensemble LSSVR [3], the closed-loop forecasting engine including KIM and EMD[4], and adaptive robust multikernel regression model [5] have been proposed to yield the research. Adaptive neurofuzzy inference systems were developed to perform a nonlinear mapping between inputs and outputs [6]
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