| 基于QPSO-RBFNN和模糊理论的电力系统短期负荷预测方法 |
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| 作者单位： | 无锡市广播电视大学 江苏无锡214021(李盘荣)，无锡市供电公司 江苏无锡214061(华伟东) |
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| 摘 要： | 在基于径向基函数神经网络(RBFNN)的电力系统短期负荷预测的基础上,采用量子粒子群优化算法(QPSO)优化神经网络权值,并运用模糊理论进行修正预测模型,提出基于QPSO-RBFNN和模糊理论的电力系统短期负荷预测方法.仿真实例计算结果表明该方法收敛速度快、预报精度高,具有工程应用前景.
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| 关 键 词： | 量子粒子群优化算法 径向基神经网络 模糊理论 电力系统 短期负荷预测 |
Short-term load forecast with QPSO-RBFNN and fuzzy logic for power systems |
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| Authors: | LI Pan-Rong HUA Wei-Dong |
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| Affiliation: | LI Pan-Rong1,HUA Wei-Dong2 |
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| Abstract: | Based on Short-term load forecast method with Radial Basis Function Neural Network(RBFNN)for power systems,a novel QPSO(quantum particle swarm optimization) algorithm is proposed for optimizing the weight of RBFNN,fuzzy theory is applied to corrected the forecast model,and a short-term load forecast method with QPSO-RBFNN and fuzzy logic is proposed in this paper.Some cases simulation results show that the method is with fast convergence,high precision,and application foreground. |
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| Keywords: | QPSO(quantum particle swarm optimization) RBF neural network fuzzy theory power system short-term load forecast |
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