【作者】 王浩；

【导师】 吴军基；

【作者基本信息】 南京理工大学 ， 电力系统及其自动化， 2013， 硕士

【摘要】 随着现代社会的快速发展,我国人民的生活质量在不断提高,在人民日常生活和国民经济建设中电能已经成为不可取代的重要能源。持续发展的国民经济,促使用电需求量飞速增长,从而导致了电力行业的发展十分迅速。电力系统短期负荷预测是电力系统经济运行和系统安全稳定运行的重要组成部分,一直以来都是电力系统中研究的重要课题。随着电力用户的复杂多样化,用户对电能质量的要求也越来越高,短期负荷预测被要求有更快的预测速度和更高的预测精度。小波分析是近年来发展十分迅速的一种时频分析工具,用于分析既含有周期分量又含有随机分量的电力负荷数据有其独特的优势,各种基于小波分析的短期负荷预测法应运而生。本文研究了基于小波包变换的综合短期负荷预测算法,采用小波包变换对负荷数据进行分解,将负荷序列分解之后,本文应用了传统BP神经网络和马尔科夫链分别进行建模预测,通过MATLAB编程对实例仿真验证,表明这两种方法都具有一定的可行性。通过研究认识到峰式马尔科夫链优于传统马尔科夫链,进而首次将其应用于电力系统短期负荷预测中,本文提出了一种改进的预测算法,即基于小波包和峰式马尔科夫链的短期负荷预测算法。使用MATLAB编程对同一实例进行了仿真,并对比预测结果,改进算法的预测精度有较大提高,验证了本文所提出的短期负荷预测算法是正确有效的。更多还原

【Abstract】 With the rapid development of modern society, the quality of Chinese people’s life is improving. Power source has become an irreplaceable important energy in people’s daily life and national economic construction. The sustainable development of the national economy is to promote the rapid growth of electricity demand, resulting in a very rapid development of power industry.Power system load forecasting is an important part of the economical and safety operation of power system, and it has always been an important issue in the study of power system. With the complexity diversification of electricity users, the requirement for the quality of power is stricter, faster rate and better accuracy are required in short-term forecasting. As a tool for frequency analysis, wavelet analysis has developed very rapidly in recent years. It has distinctive advantages in the field of analyzing load data, which contains periodic component and random component. Then, many methods of short-term load forecasting based on wavelet analysis have been creatived.This thesis studies the issue of short-term load forecasting based on wavelet packet analysis. After the load data is composed by wavelet packet analysis, two short-term load forecasting models based on BP neural network and Markov chain are proposed. Using MATALB software to simulate an instance, the two methods are proved feasible. Through the study found that the peak-type Markov chain is superior to the traditional Markov chain. Peak-type Markov chain is applied in Power system short-term load forecasting firstly in this paper. Thus an improved short-term load forecasting algorithm is proposed, which is based on wavelet packet analysis and peak-type Markov chain. Programming by MATLAB software and simulating the same distance, and comparing the predicted results. The prediction accuracy of the improved algorithm is greatly better, so the improved short-term load forecasting algorithm in this thesis is proved correct and effective.更多还原