基于改进灰色关联分析与蝙蝠优化神经网络的短期负荷预测

针对短期负荷预测的精度问题,文中提出基于改进灰色关联与蝙蝠优化神经网络的短期负荷预测方法。在传统的灰色关联分析方法基础上,引入以距离相似性和形状相近性相关联的综合灰色关联度选取更高相似度的相似日。为缩小训练样本的差异程度,提高预测精度,利用相似日集合中的样本来训练蝙蝠优化的反向传播(BP)神经网络预测模型。以中国南方某城市的历史数据作为实际算例,将文中提出的基于改进灰色关联与蝙蝠优化神经网络的短期负荷预测方法与单纯的BP神经网络法、蝙蝠优化BP神经网络法、传统灰色关联与蝙蝠优化的BP神经网络组合法的预测结果相比,结果表明文中方法的预测精度较高。

Short-term Load Forecasting Based on Improved Grey Relational Analysis and Neural Network Optimized by Bat Algorithm

In view of the accuracy of short-term load forecasting, a short-term load forecasting method based on improved grey relational analysis and back propagation(BP)neural network optimized by bat algorithm(IGRA-BA-BP)is proposed. On the basis of traditional grey relational analysis, comprehensive grey correlation degree associated with distance proximity and shape similarity is introduced to select the similar days of higher similarity. In order to reduce the difference of training samples and improve the accuracy of prediction, the samples of similar day set are used to train BP neural network prediction model which is optimized by bat algorithm. Taking historical data in a region of southern China as an actual example, the prediction results of simple BP neural network, BP neural network optimized by bat algorithm(BA-BP)and the traditional grey relational analysis and BP neural network optimized bat algorithm(GRA-BA-BP)are compared with the short-term load forecasting method based on the improved grey relational analysis and BP neural network optimized by bat algorithm, the results show that the prediction accuracy of the proposed method is better.