Univariate and multivariable forecasting models for ultra-short-term wind power prediction based on the similar day and LSTM network

Abstract

High-precision wind power prediction is an important method to ensure the safety and stability of wind power integration. However, because of the intermittent and uncontrollable nature of wind speed sequences, the wind power prediction has become a difficult task. Given this, this paper studies the prediction of wind power from three aspects. First, combining a similar day with long short-term memory is proposed to predict ultra-short-term wind power, and the improved gray correlation analysis method is used to select a similar day. In addition, to study the influence of weather data on the accuracy of the prediction model, a univariable prediction model and a multivariable prediction model are proposed to predict ultra-short-term wind power, and their performances are compared. The experimental results show that all of the above studies help improve ultra-short-term wind power prediction accuracy. Finally, the uncertainty prediction (confidence interval) of wind power is estimated by the nonparametric kernel density estimation based on the Bootstrap-Kernel density method on the result of deterministic prediction, and the upper and lower limits of wind power fluctuation are given at a certain level of confidence. The research results can provide decision-makers with accurate data changes in risk analysis and reliability assessment.