基于灰色Verhulst和灰色马尔科夫的电力负荷预测组合模型
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摘要
本文提出了电力系统中长期负荷预测的线性组合模型,即基于灰色Verhulst和灰色马尔科夫的组合模型。针对中长期负荷日趋饱和的特点,采用具有S型预测曲线的灰色Verhulst模型进行预测;针对灰色模型预测随机波动较大的负荷时拟合性较差的缺点,采用马尔科夫理论对灰色模型进行修正,弥补了灰色模型固有缺陷,提高其预测精度。通过线性组合法将灰色Verhulst模型与灰色马尔科夫模型相结合,规避了单一算法模型产生较大误差的风险,进一步提高了预测准确性。算例表明,该组合模型精度较高,具有实用性与可行性。
A linear combination model for medium and long-term power sysytem load forecasting is put forward in this paper, which based on grey Verhulst model and grey markov model. According to the characteristic of medium and long-term load has become increasingly saturated, with s-shaped prediction curve of grey Verhulst model was used to predict. On grey model has low degree of fitting when forecast the volatile objects, using markov theory to modify gery model to make up its inherent defects, and improve the prediction precision. Through the linear combination of grey Verhulst model and grey markov model, we can avoid the risks of the greater error in the single algorithm, and further improve the accuracy of forecast. The example shows that the combined model has higher accuracy, is practical and feasible.
A linear combination model for medium and long-term power sysytem load forecasting is put forward in this paper, which based on grey Verhulst model and grey markov model. According to the characteristic of medium and long-term load has become increasingly saturated, with s-shaped prediction curve of grey Verhulst model was used to predict. On grey model has low degree of fitting when forecast the volatile objects, using markov theory to modify gery model to make up its inherent defects, and improve the prediction precision. Through the linear combination of grey Verhulst model and grey markov model, we can avoid the risks of the greater error in the single algorithm, and further improve the accuracy of forecast. The example shows that the combined model has higher accuracy, is practical and feasible.
引文
[1]牛东晓.电力负荷预测技术及其应用[M].北京:中2017年第9期45国电力出版社,1998.
[2]魏少岩,吴俊勇.基于灰色模型和Kalman平滑器的多母线短期负荷预测[J].电工技术学报,2010,25(2):158-162.
[3]王泽文,张文,邱淑芳.灰色-马尔柯夫模型的改进及其参数计算方法[J].数学的实践与认识,2009,39(1):125-131.
[4]娄宝磊.灰色动态神经网络在光伏短期出力预测中的应用[J].电气技术,2017,18(2):47-51.
[5]林天祥,张宁.灰色Verhulst与等维新息组合模型在中长期负荷预测中的研究[J].电气技术,2017,18(3):42-46.
[6]尚芳屹,杨宗麟,程浩忠,等.改进Verhulst模型在饱和负荷预测中的应用[J].电力系统及其自动化学报,2015,27(1):64-68.
[7]王翠茹,孙辰军,杨静,等.改进残差灰色预测模型在负荷预测中的应用[J].电力系统及其自动化学报,2006,18(1):86-89.
[8]杨锦伟,孙宝磊.基于灰色马尔科夫模型的平顶山市空气污染物浓度预测[J].数学的实践与认识,2014,44(2):64-70.
[9]李林川,吕冬,武文杰.一种简化的电力系统负荷线性组合预测法[J].电网技术,2002,26(10):10-13.
[10]陶云奇,许江,李树春.改进的灰色马尔柯夫模型预测采煤工作面瓦斯涌出量[J].煤炭学报,2007,32(4):391-395.
[2]魏少岩,吴俊勇.基于灰色模型和Kalman平滑器的多母线短期负荷预测[J].电工技术学报,2010,25(2):158-162.
[3]王泽文,张文,邱淑芳.灰色-马尔柯夫模型的改进及其参数计算方法[J].数学的实践与认识,2009,39(1):125-131.
[4]娄宝磊.灰色动态神经网络在光伏短期出力预测中的应用[J].电气技术,2017,18(2):47-51.
[5]林天祥,张宁.灰色Verhulst与等维新息组合模型在中长期负荷预测中的研究[J].电气技术,2017,18(3):42-46.
[6]尚芳屹,杨宗麟,程浩忠,等.改进Verhulst模型在饱和负荷预测中的应用[J].电力系统及其自动化学报,2015,27(1):64-68.
[7]王翠茹,孙辰军,杨静,等.改进残差灰色预测模型在负荷预测中的应用[J].电力系统及其自动化学报,2006,18(1):86-89.
[8]杨锦伟,孙宝磊.基于灰色马尔科夫模型的平顶山市空气污染物浓度预测[J].数学的实践与认识,2014,44(2):64-70.
[9]李林川,吕冬,武文杰.一种简化的电力系统负荷线性组合预测法[J].电网技术,2002,26(10):10-13.
[10]陶云奇,许江,李树春.改进的灰色马尔柯夫模型预测采煤工作面瓦斯涌出量[J].煤炭学报,2007,32(4):391-395.