信息与电子工程2011，Vol.9Issue(5)：655-659,5.
优化粒子群算法在组合供热负荷预测中的应用
Application of particle swarm optimization algorithm in the heating load combination forecasting
高丙坤 1李阳 1许明子1
作者信息
- 1. 东北石油大学电气信息工程学院,黑龙江大庆163318
- 折叠
摘要
Abstract
This study analyzed the characteristics of the particle swarm algorithm and the combination forecast. By combining the combination forecasting with the improved particle swarm optimization, a combination of heating load forecasting model was established. For the particle swarm algorithm is liable to fall into local optimal solution, and due to its slow convergence in the later stage of evolution and other shortcomings, the standard particle swarm optimization was improved, which solved the difficulties in determining the weight coefficients. And the fitting ability of forecasting model was effectively improved as well as the accuracy of prediction. Adopting the data from daily run of a thermal substation in Daqing oilfield to verify the forecasting effect of the model, the results show that the accuracy of the combination forecasting is higher than other single forecasting methods by 40%; it has the minimum predication error.关键词
供热负荷预测/组合预测/粒子群算法/权重/预测精确度Key words
heating load forecasting/ combination forecasting/ particle swarm optimization/ weight coefficient/accuracy of prediction分类
电子信息工程引用本文复制引用
高丙坤,李阳,许明子..优化粒子群算法在组合供热负荷预测中的应用[J].信息与电子工程,2011,9(5):655-659,5.
