| 自适应粒子群优化灰色模型的负荷预测 |
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| 引用本文： | 尹新,周野,何怡刚,陈建.自适应粒子群优化灰色模型的负荷预测[J].电力系统及其自动化学报,2010,22(4). |
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| 作者姓名： | 尹新 周野 何怡刚 陈建 |
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| 作者单位： | 湖南大学电气与信息工程学院,长沙,410082 |
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| 基金项目： | 国家高技术研究发展计划(863)，国家自然科学基金，高等学校博士学科点专项科研基金 |
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| 摘 要： | 针对传统灰色预测模型GM(1,1)在预测增长较快的电力负荷时预测效果变差这一局限性,引入了比标准粒子群优化算法效率更高的自适应粒子群优化算法,并与GM(1,1)模型相结合,利用自适应粒子群算法求解GM(1,1)模型中的参数a和u,提出一种自适应粒子群优化灰色模型.通过对四个地区的用电量进行实例仿真,证明该模型具有较广的适用范围和较高的预测精度.
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| 关 键 词： | 电力负荷预测 灰色模型 自适应 粒子群优化 |
Power Load Forcasting Based on Adaptive Particle Swarm Optimization Grey Model |
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| YIN Xin,ZHOU Ye,HE Yi-gang,CHEN Jian.Power Load Forcasting Based on Adaptive Particle Swarm Optimization Grey Model[J].Proceedings of the CSU-EPSA,2010,22(4). |
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| Authors: | YIN Xin ZHOU Ye HE Yi-gang CHEN Jian |
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| Affiliation: | YIN Xin,ZHOU Ye,HE Yi-gang,CHEN Jian(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China) |
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| Abstract: | When the power load forecasting grows quick,the traditional gray prediction model GM(1,1,)becomes worse.In order to overcome the limitation,an adaptive particle swarm optimization algorithm is introduced,which is more efficient than the standard particle swarm.Combined with the GM(1,1)model and the adaptive particle swarm algorithm for GM(1,1)model parameters,the optimization algorithm is promoted.Simulation results of electricity consumption in four cities show that the model has a wider scope and higher p... |
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| Keywords: | power load forecasting grey model adaptive particle swarm optimization |
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