| 考虑相似日的短期负荷改进粒子群预测方法 |
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| 引用本文： | 侯宜祥. 考虑相似日的短期负荷改进粒子群预测方法[J]. 安徽电气工程职业技术学院学报, 2010, 15(3): 57-59. DOI: 10.3969/j.issn.1672-9706.2010.03.014 |
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| 作者姓名： | 侯宜祥 |
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| 作者单位： | 安徽省电力公司培训中心,安徽,合肥,230022;安徽电气工程职业技术学院,安徽,合肥,230022 |
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| 摘 要： | 短期负荷预测是电力系统安全经济运行管理的一个基本环节。提出了基于相似日和改进粒子群算法的短期负荷预测方法,在相似负荷曲线中寻找最佳预测负荷曲线,并采用随机变异机制增强粒子群体的多样性。仿真算例验证了上述算法的有效性。
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| 关 键 词： | 短期负荷预测 改进粒子群算法 相似日 |
Short Term Load Forecasting Based on Advanced Particle Swarm Algorithm and Similar Day's Load |
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| HOU Yi-xiang. Short Term Load Forecasting Based on Advanced Particle Swarm Algorithm and Similar Day's Load[J]. Journal of Anhui Electrical Engineering Professional Technique College, 2010, 15(3): 57-59. DOI: 10.3969/j.issn.1672-9706.2010.03.014 |
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| Authors: | HOU Yi-xiang |
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| Affiliation: | HOU Yi-xiang1,2(1.Training Center of Anhui Electric Power Corporation,Hefei 230022,China,2.Anhui Electrical Engineering Professional Technique College,China) |
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| Abstract: | Short term load forecasting is one of security and operation managements of power system.A short term load forecasting method based on similar day′s load and advanced particle swarm algorithm is proposed.An optimal load curve is selected from similar day′s load curves.Stochastic mutation mechanism is adopted to enhance particles′ diversity.The validity of the above method is verified by a testing example. |
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| Keywords: | short term load forecasting advanced particle swarm algorithm similar day′s load |
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