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
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- 引用格式:
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刘金源,王寿喜,李婵.城市燃气日负荷PCA-GM-BPNN组合预测模型[J].天然气与石油,2018,36(5):0.doi:
Liu Jinyuan, Wang Shouxi, Li Chan.PCA-GM-BPNN Combined Forecasting Model for Daily Load of City Gas[J].Natural Gas and Oil,2018,36(5):0.doi:
- DOI:
- 作者:
- 刘金源1 王寿喜1,2 李 婵3
Liu Jinyuan1, Wang Shouxi1,2, Li Chan3
- 作者单位:
- 1. 西南石油大学石油与天然气工程学院, 2. 西安石油大学石油工程学院3. 中国石油天然气股份有限公司天然气销售广东分公司,
1. School of Petroleum and Natural Gas Engineering, Southwest Petroleum University； 2.School of Petroleum and Natural Gas Engineering, Xi’an Shiyou University； 3.PetroChina Natural Gas Sales Guangdong Branch
- 关键词:
- 主成分分析;GM灰色预测模型;BP神经网络模型;日负荷;负荷预测
Principal component analysis; GM (1, 1) model; BP neural network; Daily load; Load forecasting
- 摘要:
- 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PCAGMBPNN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 Principal Component Analysis (PCA), Grey Model (GM) and BP Neural Network (BPNN).The model first uses the grey optimization model to predict the prediction value that is equal to the number of training samples of BP neural network as the correction sequence of the input layer neurons,then through PCA to reduce the dimension of the impact factors including the correct 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 5.44%, which is less than the other four load forecasting models, and it is a rather effective method for daily load forecasting of city gas.
