Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

Zexian Sun, Hexu Sun, Jingxuan Zhang

Mathematical Problems in Engineering · 24 July 2018

In one sentence. Predictive deep belief network feeds wind speed forecasts into an optimised random forest for wind power prediction at 15 min to 24 h horizons.

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.

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