为提升超短期风电功率的预测精度,提出一种加入融合柯西变异和反向学习策略的改进鱼鹰优化算法（IOOA）,用于优化以长短期记忆网络（LSTM）和变模态分解（VMD）为基础的组合预测模型。首先,采用变模态分解收集的历史风电功率数据, 将非线性较强的原始功率数据分解为较为稳定的子序列。其次,使用改进鱼鹰优化算法对长短期记忆网络的隐藏单元数目、训练周期、初始学习率3个参数进行寻优。最后,使用长短期记忆网络对各子序列预测,将各子序列预测值叠加起来得到最终结果。通过风电场实测数据仿真分析,相比于普通长短期记忆网络模型的预测结果,所提模型的均方根误差下降了62.5%、平均绝对百分比误差和平均绝对误差分别下降了61.1%和55.9%,预测精度也高于其他4种组合预测模型,表明该模型成功提高了超短期风电功率的预测精度。
Abstract
To enhance the accuracy of ultra-short-term wind power, a new optimization algorithm called improved osprey optimization algorithm （IOOA） is proposed. It combines Cauchy mutation and reverse learning strategy to optimize a combination prediction model based on long short-term memory network （LSTM） and variable mode decomposition （VMD）. Firstly, the historical wind power data collected through VMD is used to decompose the original power data with strong nonlinearity into relatively stable subsequences. Secondly, the osprey optimization algorithm, combining Cauchy mutation and reverse learning strategies, is used to optimize the number of hidden units, training period, and initial learning rate of the LSTM. Finally, the final prediction result is obtained by using a LSTM to predict each subsequence, and the predicted values of each subsequence were superimposed to obtain the final result.The proposed wind farm prediction model has been analyzed by using measured data. The results were compared with ordinary short-term memory neural network models. The proposed model reduced the RMSE by 62.5%, decreased MAPE by 62.2% and MAE by 55.9%. And the prediction accuracy is also higher than other four combination prediction models, indicating its success in improving the prediction accuracy of short-term wind power.
关键词
长短期记忆网络 /
变模态分解 /
风力发电 /
改进鱼鹰优化算法 /
功率预测 /
优化算法
Key words
long short-term memory network /
variable mode decomposition /
wind power /
improved osprey optimization algorithm /
power prediction /
optimization algorithm
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基金
贵州省科技支撑计划（黔科合支撑[2023]一般411; 黔科合支撑[2023]一般412; 黔科合支撑[2024]一般051）; 贵州省双碳研究院开放课题（DCRE-2023-13）
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