基于LSTM循环神经网络算法的风电预测技术

风力发电的随机性和不可控性,给发电控制环节造成控制负担,利用不同高度的风速、风向等气候因素对风力发电数据进行准确预测,有利于制定合理的调度计划.本文使用LSTM循环神经网络算法,实现了单变量预测未来一个时间点、多变量预测未来一个时间点的风电预测实例验证,实验结果发现算法在两种情况下均保持着高的预测精度.且由于单变量和多...

Wind power forecasting technology based on LSTM recurrent neural network algorithm

The randomness and uncontrollability of wind power generation puts a control burden on the power generation control link.The use of wind speed and wind direction at different heights to accurately predict wind power data is conducive to formulating a reasonable dispatch plan.In this paper,the LSTM recurrent neural network algorithm is used to realize the verification of wind power forecasting examples in which univariate predicts a point in the future and multivariate predicts a point in the future.The experimental results show that the algorithm maintains high prediction accuracy in both cases.And because the prediction error is not much different between the univariate and the multivariate cases,it shows that the wind speed itself still plays a decisive role in the multivariate data in the actual wind power forecasting.