| 基于改进粒子群-径向基神经网络模型的短期电力负荷预测 |
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| 引用本文： | 师彪,李郁侠,于新花,闫旺,何常胜,孟欣.基于改进粒子群-径向基神经网络模型的短期电力负荷预测[J].电网技术,2009(17). |
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| 作者姓名： | 师彪 李郁侠 于新花 闫旺 何常胜 孟欣 |
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| 作者单位： | 西安理工大学水利水电学院;青岛科技大学高职技术学院; |
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| 基金项目： | 国家火炬计划创新基金(07C26213711606); 陕西省自然科学基础研究计划基金(SJ08E220); 山东省软科学基金(2007RKB188) |
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| 摘 要： | 为了准确、快速、高效地预测电网短期负荷,提出了改进的粒子群–径向基神经网络算法。用改进的粒子群算法训练径向基神经网络,实现了径向基函数神经网络的参数优化。建立了短期电力负荷预测模型,综合考虑气象、天气、日期类型等影响负荷的因素进行短期负荷预测。算例结果表明,该算法优于径向基神经网络法和粒子群–径向基网络算法,克服了径向基网络和粒子群优化方法的缺点,改善了径向基神经网络的泛化能力,输出稳定,预测精度高,收敛速度快,平均百分比误差可控制在1.2%以内。
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| 关 键 词： | 负荷预测 改进粒子群–径向基神经网络模型 泛化能力 预测精度 |
Short-Term Load Forecasting Based on Modified Particle Swarm Optimization and Radial Basis Function Neural Network Model |
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| SHI Biao,LI Yu-xia,YU Xin-hua,YAN Wang,HE Chang-sheng,MENG Xin.Short-Term Load Forecasting Based on Modified Particle Swarm Optimization and Radial Basis Function Neural Network Model[J].Power System Technology,2009(17). |
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| Authors: | SHI Biao LI Yu-xia YU Xin-hua YAN Wang HE Chang-sheng MENG Xin |
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| Affiliation: | SHI Biao1,LI Yu-xia1,YU Xin-hua2,YAN Wang1,HE Chang-sheng1,MENG Xin1 (1.School of Water Resources and Hydraulic Power,Xi'an University of Technology,Xi'an 710048,Shaanxi Province,China,2.Technical Instisute of High Vocation,Qingdao Science and Technology University,Qingdao 261000,Shandong Province,China) |
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| Abstract: | To forecast short-term power load fast,accurately and efficiently,the features and defects of particle swarm optimization (PSO) algorithm are analyzed and an modified PSO-radial basis function neural network (RBFNN) algorithm is proposed,in which the RBFNN is trained by improved PSO to implement the optimization of RBFNN parameters and a short-term load forecasting model is built. In load forecasting such factors impacting loads as meteorology,weather and date types are comprehensively considered. Calculati... |
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| Keywords: | load forecasting modified particle swarm optimization and radial basis function neural network model generalization ability forecasting precision |
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