Short-term wind power probability density prediction based on long short term memory network quantile regression

Wind power forecasting is of great significance for the safe and stable operation of power systems and the optimal allocation of energy. Aiming at the fact that the wind power deterministic prediction cannot give the risk assessment of the forecast results and the existing static prediction model is difficult to describe the long-term correlation of wind power. A short-term wind power probability density prediction based on long short-term memory network quantile regression(LSTMQR) is proposed. The method first uses the long short-term memory network quantile regression to obtain the prediction results of future wind power under different quantile points. Secondly, combining LSTMQR with kernel density estimation (KDE) for short-term wind power probability prediction, the probability density function of future wind power prediction points can be obtained. The model is validated by a number of evaluation indicators based on the measured data of a domestic wind farm. The results show that the proposed model is better than the benchmark model. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.