Short-term Wind Power Prediction Method Based on UAV Patrol and Deep Confidence Network

Abstract

At present, wind power has become the most promising energy supply. However, the intermittent and fluctuating wind power also poses a huge challenge to accurately adjust the electrical load. In order to find a method capable of forecasting wind power generation in a short period of time, we propose a short-term wind power generation forecasting method based on an optimized deep belief network approach. Based on GEFCom2012 competition dataset, by continuously tuning the parameters of the deep belief network for 15 sets of experiments, we obtained three optimal laboratory combinations: Experiment 4, Experiment 10, and Experiment 12. The results show that the R-squared values of Experiment 4, Experiment 10 and Experiment 12 are the highest, which are 0.955, 0.93 and 0.98, respectively. The average R-squared value of these three tuned experiments is 0.2342 higher than the average of the other 12 experiments. At the same time, it is concluded that when the learning frequency is low, the linear function can learn the most obvious features more directly; When the learning frequency is high, the nonlinear function can learn the internal latent features more directly.