| 引用本文: | 琚垚,祁林,刘帅.基于改进乌鸦算法和ESN神经网络的短期风电功率预测[J].电力系统保护与控制,2019,47(4):58-64. |
| JU Yao,QI Lin,LIU Shuai.Short-term wind power forecasting based on improved crow search algorithm and ESN neural network[J].Power System Protection and Control,2019,47(4):58-64 |
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| 摘要: |
| 精确的短期风电功率预测对于提升电力系统经济稳定运行十分重要。为了克服传统的神经网络在参数选取中容易受主观因素影响和陷入局部最优的不足，提出一种基于改进乌鸦算法(ICSA)优化回声状态神经网络(ESN)参数的短期风电功率组合预测方法。在算法寻优初期引入Lévy飞行机制增强搜索效率，而在迭代后期加入高斯函数，对进化后的全部轨迹进行相应的调整，保证算法的全局寻优和逐次逼近能力；通过改进的CSA算法对ESN神经网络输出层连接权值矩阵进行优化以提高网络的训练效率。最后利用两组实验数据对预测模型进行了有效性验证，结果表明，所提算法能有效应对风电功率时序的随机性和不确定性特征，具有更高的建模精度和更快的收敛速度。 |
| 关键词: 乌鸦算法 Lévy飞行 ESN神经网络 高斯函数 风电功率预测 |
| DOI：10.7667/PSPC180251 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(51677072) |
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| Short-term wind power forecasting based on improved crow search algorithm and ESN neural network |
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JU Yao1,QI Lin2,LIU Shuai3
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(1. Henan Xinxiang Vocational and Technical College, Xinxiang 453000, China;2. Henan University of Urban Construction, Pingdingshan 467036, China;3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China)
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| Abstract: |
| Accurate short-term wind power forecasting is important for improving the economic and stable operation of power system. Since it is easy to be affected by subjective factor and fall into local optimum in parameters selecting compared with traditional Neural Network, a novel combination forecasting approach based on Improved Crow Search Algorithm (ICSA) to optimize the parameters of Echo State Network (ESN) neural network is proposed to overcome above inadequacies. The Lévy flight is introduced to increase the searching efficiency at initial stages, and during the later stage of iteration, the Gauss function is added aiming at making an appropriate adjustments for the whole trajectory points after evolution, which can guarantee the ability of global optimization and successive approximation; it chooses optimal the weight values of the hidden layer to enhance the efficiency of neural network training by ICSA algorithm. Finally, effectiveness of the proposed forecasting model is tested on two groups of experimental data, the results show that proposed algorithm can effectively cope with the variability and intermittency of wind power time series, having higher modeling precision and faster convergence speed. This work is supported by National Natural Science Foundation of China (No. 51677072). |
| Key words: crow search algorithm (CSA) Lévy flight ESN neural network Gauss function wind power forecasting |
