基于改进MFO优化Attention-LSTM的超短期风电功率预测*

doi:10.11985/2023.03.038

摘要/Abstract

摘要：

针对风电功率的不确定性问题，提出一种基于改进飞蛾扑火算法(Moth-flame optimization，MFO)优化注意力机制长短时神经网络(Attention long short-term memory，Attention-LSTM)的风电功率超短期预测方法。首先利用自适应噪声完全集合经验模态分解(Complete ensemble empirical mode decomposition with adaptive noise analysis，CEEMDAN)将原始功率数据分解为多个模态分量(Intrinsic mode functions，IMF)，以消除不同分量间的影响，再计算各个分量的样本熵，将样本熵近似的值合并，以降低运算规模；然后，通过引入Chebyshev混沌映射、柯西变异、基于Sigmoid函数的惯性权值来对传统的飞蛾扑火算法进行改进，并将改进的飞蛾扑火算法与传统MFO、粒子群算法(Particle swarm optimization，PSO)进行比较，证明其寻优能力有了很大提升；最后，将Attention机制用于计算LSTM神经网络隐层状态的不同权重，利用改进飞蛾扑火优化算法优化Attention-LSTM的超参数，分别对合并后的IMF分量进行建模，将各分量模型叠加得到最终功率预测曲线。对锦州某风电场的功率实测数据进行仿真分析，结果表明，所提模型具有较高的预测精度，对实际工程具有一定的借鉴意义。

关键词: 风电功率, Attention机制, 混沌映射, 柯西变异, 改进MFO算法, LSTM

Abstract:

For the uncertainty of wind power, an ultra short-term wind power prediction method based on improved MFO(Moth-flame optimization) attention LSTM(Long short-term memory) is proposed. Firstly, the original power data is decomposed into multiple IMF(Intrinsic mode functions) components by using the adaptive noise complete ensemble empirical mode decomposition (CEEMDAN) to eliminate the influence between different components, calculate the sample entropy of each component, and combine the approximate values of sample entropy to reduce the operation scale. Secondly, by introducing Chebyshev chaotic mapping, Cauchy mutation, Sigmoid function-based inertia weights to improve the traditional moth fire fighting algorithm, and the improved moth fire fighting algorithm is compared with the traditional MFO, PSO(Particle swarm optimization) algorithm. It is proved that its optimization ability has been greatly improved. Finally, the Attention mechanism is used to calculate the different weights of the hidden layer state of the LSTM neural network, and the improved moth-fighting optimization algorithm is used to optimize the hyperparameters of the Attention-LSTM. The combined IMF components are modeled respectively, and each component model is superimposed to obtain the final power prediction curve. Through the simulation analysis of the measured data of a wind farm in Jinzhou, the results show that the proposed model has high prediction accuracy and has certain reference significance for actual engineering.

Key words: Wind power, Attention mechanism, chaos mapping, Cauchy mutation, improved MFO algorithm, long short-term memory

中图分类号:

TM614

宋立业, 鞠亚东, 张鑫. 基于改进MFO优化Attention-LSTM的超短期风电功率预测^*[J]. 电气工程学报, 2023, 18(3): 358-368.

SONG Liye, JU Yadong, ZHANG Xin. Ultra-short-term Wind Power Prediction Based on Improved MFO Optimized Attention-LSTM[J]. Journal of Electrical Engineering, 2023, 18(3): 358-368.

图/表 22

图1

图2

图3

图4

图5

表1

四种测试函数"

测试函数	维度	搜索范围
$f 1 x = ∑ i = 1 n x i + ∏ i = 1 n x i$	30	[-10，10]
$f 2 x = ∑ i = 1 n x i 4 + random 0, 1$	30	[-1.28，1.28]
$f 3 x = 1 4 000 ∑ i = 1 n x i 2 − ∏ i = 1 n cos x i i + 1$	30	[-600，600]
$f 4 x = ∑ i = 1 n x i − 10 cos 2 π x i + 10$	30	[-5.12，5.12]

表1

图6

图7

图8

图9

表2

图10

图11

图12

图13

表3

图14

图15

图16

图17

图18

表4

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算法类型	相同迭代次数(300次)算法寻优结果
算法类型	f₁函数	f₂函数	f₃函数	f₄函数
改进MFO	0	2.53×10^-8	0	1.1×10^-274
MFO	0.005 25	4.02×10^-5	1.9×10^-5	0.009 831
PSO	109.863 5	1.97×10^-6	1.48×10^-23	4×10^-14
算法类型	相同收敛精度(10^-6)算法迭代次数(最大300次)
算法类型	f₁函数	f₂函数	f₃函数	f₄函数
改进MFO	7	98	11	7
MFO	未达到	未达到	67	94
PSO	未达到	未达到	未达到	299

模型	MAPE(%)	RMSE/kW
LSSVM	16.38	3.122
LSTM	12.28	1.718
Attention-LSTM	9.77	0.806
MFO-Attention-LSTM	6.88	0.289
改进MFO-Attention-LSTM	4.22	0.124