摘要
天然气是一种重要的清洁能源,如何预知未来时刻的天然气负荷,对于燃气公司与上游气源及下游用户制定合适的商业计划具有重要的意义.针对天然气短期负荷预测问题,提出基于混沌理论和Volterra自适应滤波器的预测模型.首先,对天然气时负荷时间序列进行日相关性分析,然后采用互信息法和伪最近邻域法确定延迟时间和最佳嵌入维数;其次,在相空间重构的基础上,对天然气时负荷时间序列进行混沌特性分析;再次,针对现有预测模型多为主观预测模型的特点,将Volterra自适应滤波器预测模型引入到天然气时负荷预测中,降低了人为主观性;最后,给出预测算例,探讨不同Volterra自适应滤波器阶数对预测效果的影响及对比了Volterra自适应滤波器预测模型与人工神经网络(artificial neural network,ANN)、傅里叶级数预测模型的预测精度,验证二阶Volterra自适应滤波器预测模型在不同日时负荷的预测效果.结果表明:二阶Volterra自适应滤波器预测模型较ANN、傅里叶级数预测模型具有更高的预测精度,为天然气短期负荷的在线工程应用提供了有益参考.
Gas is an important source of clean energy, so in the future, gas load forecasting will have a very important meaning for the gas company to develop an appropriate business plan between ‘upstream＇ gas supplier and ‘downstream＇ users. Aiming at the gas short-term load forecasting problem, a prediction model has been put forward based on chaotic theory and Volterra adaptive filter. Firstly, conduct day-to-day correlation analysis for collected gas hour load data, and then calculate delay times and find out the optimal embedding dimension by the approach of mutual information and pseudo-nearest-neighbor. Secondly, on the basis of phase-space reconstruction, carry out chaotic characteristic analysis for collected gas hour load data. Once again, aiming at the current situation of most existing prediction models being subjective, reduce the subjectivity in the process of gas load forecasting by introduction of Volterra adaptive filter prediction model. Finally, the influence of the different order of Volterra adaptive filter on prediction results, and the comparison of accuracy among the Volterra adaptive filter prediction model, ANN（artificial neural network, ANN） prediction model and Fourier series prediction model are discussed through gas load forecasting example. Additionally, the forecasting results of the second order Volterra adaptive filter prediction model showed that： compared with ANN prediction model and fourier series prediction model, the second order Volterra adaptive filter prediction model has higher accuracy, and may provide a useful reference for practical engineering applications of short-term gas load forecasting.
出处
《中国科学：技术科学》
EI
CSCD
北大核心
2015年第1期91-102,共12页
Scientia Sinica(Technologica)
基金
中国石油集团公司重点研究项目(批准号:KY2011-13)
国家自然科学基金(批准号:51474187)资助项目
