基于小波长短期记忆网络的风电功率超短期概率预测

Ultra-short-term probability prediction of wind power based on wavelet decomposition and long short-term memory network

随着大规模的风电并网,风电所具有的间歇性与随机性对电力系统的稳定性产生了很大的影响,风电功率预测成为当前解决该问题重要的方式之一.本文利用长短期记忆(LSTM)网络良好的时序记忆特性,将小波分解技术与LSTM深度网络结合,提出基于小波长短期记忆网络的风电功率超短期概率预测模型.首先通过小波分解技术将原始时间序列进行平稳化处理,再建立各子序列样本的LSTM网络预测模型,借助最大似然估计法估计预测误差的高斯分布函数,最终实现对未来4 h时刻的风电功率概率区间预测.最后,采用中国东北某风电场数据对所提方法进行算例分析,结果表明,将小波分解与深度学习方法结合可以较好地提高预测的精度,提高概率预测的区间可靠性.

With the large-scale wind power connected into the power systems,the intermittency and randomness of wind power have a great impact on the stability of the power systems.Therefore,the accurate prediction of wind power has become one of the most important ways to solve this problem.In this paper,considering the timing memory characteristics of long short-term memory(LSTM) network,by combining wavelet decomposition and LSTM network,an ultra-short-term probability prediction model for wind power based on wavelet-LSTM network is proposed.Firstly,wavelet decomposition is used to smooth the sequence of the original time sequence.Then the LSTM network prediction model for the sequence samples is developed.By using the maximum likelihood estimate method,the Gaussian distribution function of prediction error can be estimated.Thus the probability prediction of wind power in the future 4 hours could be realized.Finally,based on the wind farm data in Northeast China,simulation results show that wavelet decomposition with deep learning method can improve the accuracy of prediction.The interval reliability of probability prediction is also improved.