城市燃气日负荷PCA-GM-BPNN组合预测模型

PCA-GM-BPNN Combined Forecasting Model for Daily Load of City Gas

城市燃气日负荷预测准确性,对燃气供应系统的优化设计、合理调度和稳定运行具有重要意义。基于BP神经网络、GM灰色预测理论和PCA主成分分析三种模型,综合考虑负荷预测的诸多影响因素,建立城市燃气日负荷PCA-GM-BPNN组合预测模型。该组合模型首先利用灰色优化模型预测出BP神经网络所需的样本校正序列,然后应用主成分分析技术对包括校正序列在内的日负荷影响因子进行降维处理,再将降维后累计贡献率占比85以上的几种主成分作为输入层神经元输入神经网络进行训练。通过实际应用效果分析可知,该组合模型预测的南乐县燃气日负荷MAPE值为4. 06,均小于其他四种负荷预测模型,是一种更为有效的城市燃气日负荷预测方法。

The accuracy of city gas daily load forecasting is of great significance to the optimal design,rational scheduling and stable operation of gas supply system.This paper comprehensively considers many factors of daily load forecasting and establishes a combined forecasting model PCA-GM-BPNN,which is based on BP Neural Network,GM Grey Prediction and Principal Component Analysis.The model first uses the grey optimization model to predict the sample correction sequence needed for BP neural network,then uses the principal component analysis technology to reduce the dimension of the daily load impact factors including the correction sequence,and then several principal components with more than 85 of the cumulative contribution rate are trained as input neural networks for neural network training.The analysis of practical application results shows that the daily load MAPE value of Nanle county in this combined model is 4.06,which is less than the other four load forecasting models,and it is a rather effective method for daily load forecasting of city gas.