| 优化粒子群算法在组合供热负荷预测中的应用 |
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| 引用本文： | 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]. 太赫兹科学与电子信息学报, 2011, 9(5): 655-659 |
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| 作者姓名： | 高丙坤 李 阳 许明子 |
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| 作者单位： | 东北石油大学电气信息工程学院,黑龙江大庆,163318 |
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| 摘 要： | 分析了粒子群算法和组合预测的特点。将组合预测和粒子群算法结合,建立了一种组合形式的供热负荷预测模型。同时针对粒子群算法易于陷入局部最优解、进化后期收敛慢等缺点对粒子群算法进行改进,解决了组合预测中权重难以确定的问题。改善了预测模型的拟合能力,提高了预测精确度。最后选取大庆油田某一天供热数据作为测试数据,结果表明组合预测误差较小,精确度高于其他单项预测方法40%以上。
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| 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 |
| 收稿时间： | 2011-06-30 |
| 修稿时间： | 2011-07-18 |
Application of particle swarm optimization algorithm in the heating load combination forecasting |
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| GAO Bing-kun,LI Yang and XU Ming-zi. Application of particle swarm optimization algorithm in the heating load combination forecasting[J]. Journal of Terahertz Science and Electronic Information Technology, 2011, 9(5): 655-659 |
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| Authors: | GAO Bing-kun LI Yang XU Ming-zi |
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| Affiliation: | GAO Bing-kun,LI Yang,XU Ming-zi(School of Electrical and Information Engineering,Northeast Petroleum University,Daqing Heilongjiang 163318,China) |
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| 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 determi... |
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| Keywords: | heating load forecasting combination forecasting particle swarm optimization weight coefficient accuracy of prediction |
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