基于布谷鸟-Elman算法的光伏发电预测

Photovoltaic power prediction based on Elman neural network with improved cuckoo algorithm

提高光伏发电功率预测的精度对于保证电网的安全稳定运行、提高光伏资源的开发和利用率具有重要的意义。文中提出了一种基于天气相似度以及改进布谷鸟算法优化Elman神经网络的光伏发电短期功率预测模型。首先在选取相似日上,提出一种基于距离和角度趋势的相似度计算方法,选出与待预测日相似度更高的相似日。其次,利用改进后的布谷鸟算法对Elman神经网络的权值和阈值进行优化并构建光伏发电短期功率预测模型。最后将文中提出的光伏发电预测模型与传统Elman神经网络模型的预测结果及实际输出值进行比较,结果表明改进布谷鸟算法优化Elman神经网络的光伏发电短期功率预测模型预测精度更高。

Improving the accuracy of photovoltaic power generation prediction has important significance for ensuring the security and stability of power system and improving the development and utilization of solar energy resources.A short-term power forecasting model for photovoltaic power generation is proposed in this paper.This model is based on weather similiarty degree and improved cuckoo search algorithm(ICS),which is used to optimize Elman neural network.Firstly,in order to select the similar days with higher similarity,a similarity calculation method based on distance and angle trend is proposed.Secondly,the improved cuckoo algorithm is used to optimize the weight and threshold of Elman neural network,and a short-term power prediction model for photovoltaic power generation is established.Finally,the prediction results of the proposed model is compared with the results of traditional model.The results show that the prediction accuracy of this method is higher.