优秀论文及配套源码 SVM Short-term-Load-Forecasting

  优秀论文及配套源码。首先阐述了负荷预测的应用研究现状，概括了负荷预测的特点及其影响因素，归纳了短期负荷预测的常用方法，并分析了各种方法的优劣；接着介绍了作为支持向量机（SVM）理论基础的统计学习理论和SVM的原理，推导了SVM回归模型；本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized )

AdaptFunc.m,2392,2010-06-04
AdaptFunc1.m,2386,2010-06-17
BaseStepPso.m,3813,2010-05-18
gaijin.m,2725,2010-06-04
InitSwarm.m,2174,2010-05-18
pso.m,2112,2010-06-03
shorttime.m,2527,2010-06-17
基于支持向量机的短期电力负荷预测.doc,959488,2010-11-18
数据
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....\a45.xls,20992,2010-06-03
....\B2.xls,16896,2010-06-03
....\b3.xls,19456,2010-05-17
....\B4.xls,19456,2010-06-03
....\B5.xls,17408,2010-06-03
....\bdata1.xls,35840,2010-03-12