带扩展记忆的粒子群优化算法及其在电力系统短期负荷预测中应用控制科学与工程专业论文.docxVIP

重庆大学硕士学位论文英 重庆大学硕士学位论文 英文摘要 PAGE PAGE IV strong grasp the influences of environmental factors. EMD (Empirical Mode Decomposition) is employed to decompose the nonlinear and non-stationary power load series into IMFs (Intrinsic Mode Functions) and the residue during the parse of data preprocessing. Analysis of IMFs and the residue disclose the character of load variation and the effect of environment factors. Then choose appropriate kernel functions, build prediction models respectively. All components’ prediction results are reconstructed and the final results are gained. In view of the reduction of computing effort and the increase of computing efficiency, characteristics of IMFs and the residue are summarized and flow of prediction is simplified. The simulation example of the EUNITE (Uropean Network on Intelligent TEchnologies for Smart Adaptive Systems) power load prediction competition verifies the effectiveness of the proposed prediction model. Keywords: Particle Swarm Optimization with Extended Memory, Load Forecasting, Support Vector Machine, Empirical Mode Decomposition. 重庆大学硕士学位论文目录 重庆大学硕士学位论文 目录 目录 中文摘要I 英文摘要 II HYPERLINK \l _bookmark0 1 绪论 1 HYPERLINK \l _bookmark1 1.1 短期电力负荷预测的目的和意义 1 HYPERLINK \l _bookmark2 1.2 短期电力负荷预测的发展和研究现状 2 HYPERLINK \l _bookmark3 1.3 课题研究的主要内容 4 HYPERLINK \l _bookmark4 1.4 小结 4 HYPERLINK \l _bookmark5 2 短期负荷预测模型分析 5 HYPERLINK \l _bookmark6 2.1 负荷预测的特点和原理 5 HYPERLINK \l _bookmark7 2.1.1 负荷预测的特点 5 HYPERLINK \l _bookmark8 2.1.2 负荷预测的原理 6 HYPERLINK \l _bookmark9 2.2 负荷预测的影响因素 7 HYPERLINK \l _bookmark10 2.3 负荷预测的常用模型 8 HYPERLINK \l _bookmark11 2.3.1 数学统计方法 8 HYPERLINK \l _bookmark12 2.3.2 人工智能方法 12 HYPERLINK \l _bookmark13 2.4 预测模型的选择 14 HYPERLINK \l _bookmark14 2.5 小结 15 HYPERLINK \l _bookmark15 3 基于 PSOEM 和 SVM 的电力系统短期负荷预测模型 16 HYPERLINK \l _bookmark16 3.1 支持向量机回归理论 16 HYPERLINK \l _bookmark17 3.2 支持向量机回归中的参数作用 18 HYPERLINK \l _bookmark18 3.3 带扩展记忆的粒子群优化算法 19 TOC \o 1-2 \h \z \u HYPERLIN