【作者】 王李东；

【导师】 文劲宇；

【作者基本信息】 华中科技大学 ， 电力系统及其自动化， 2005， 硕士

【摘要】 短期负荷预测是电力系统安全经济运行的前提,随着分时电价方式的推广和电力市场化改革的深入,电力公司力求及时、准确地把握负荷变化的信息,对负荷预测的重要性和迫切性提到了前所未有的高度,同时也对负荷预测的精度提出了更高的要求,这必将推动我国对负荷预测新方法、新技术的研究。现有研究表明,人工神经网络是一种较好的短期负荷预测的方法,但是,人工神经网络存在数学理论基础不确定、收敛速度慢、难以找到全局最优解等不足,限制了它在实际电力系统中的应用和推广。最近一些学者提出了支持向量机(Support Vector Machine, SVM)算法,SVM 是基于结构风险最小化原则的新型机器学习方法,有着比传统的基于经验风险最小化原则的人工神经网络更优越的性能。因此,本文将SVM 引入短期负荷预测,以期开发出一种性能更加优越的短期负荷预测方法。论文首先全面、系统地阐述了负荷预测和SVM 的应用研究现状,概括了负荷预测的特点、要求及其影响因素,归纳了短期负荷预测常用的模型和方法,并分析了各种方法的优劣; 接着详细介绍了作为SVM 理论基础的统计学习理论和SVM 的原理,推导了SVM 分类模型和回归模型,对其中的关键问题进行了细致的研究; 之后将SVM 引入短期负荷预测,为提高预测速度,根据短期负荷预测的特点,设计了一种新的在线训练算法,该算法通过不断输入新的负荷数据来更新回归函数,以获得更快的计算速度和较好的预测精度,和常规的SVM 回归算法相比,新算法能在保证精度的同时大大减少支持向量的数目,从而具有更快的收敛性,仿真结果证明了该方法的有效性; 论文最后对主要工作和研究内容进行了总结,并提出了有待进一步研究的问题。更多还原

【Abstract】 The daily operation and planning activities of an electric utility requires the prediction of the electrical demand of its customers. In general, the required load forecasts can be categorized into short-term, mid-term, and long-term forecasts. The quality of short-term load forecasts has a significant impact on the economic operation of the electric utility since many decisions based on these forecasts have significant economic consequences. The importance of accurate load forecasts will increase in the future because of the dramatic changes occurring in the structure of the utility industry due to deregulation and competition. This environment compels the utilities to operate at the highest possible efficiency, which as indicated above requires accurate load forecasts. Conventional short term load forecasting models includes statistical models, autoregressive and moving averages, spectral expansion techniques and Kalman filters. Because these models can not adapt easily to rapid changes of the load variation pattern, they have deficiencies. In the past decades, artificial neural networks (ANN) which is based on empirical risk minimization (ERM) principle have been successfully used for short term load forecasting. However, the selection of training data and network architecture significantly effects the performance of the neural network approach, and a trained network may become obsolete with abrupt changes in load conditions thereby making the forecaster inaccurate. Recently, a novel learning technique called support vector machine (SVM) has been put forward. SVM is based on structural risk minimization (SRM) principle and has shown many advantages compared with ANN. This thesis concentrates in implementing SVM to short term load forecasting. The statistical learning theory which is the basis of SVM is introduced firstly, then the detailed procedures of using SVM for classification problem and for regressive problem are given. The effects and selection of some important parameters of SVM are discussed. After that an online training algorithm for short term load forecasting based on SVM is proposed. In the online training algorithm, the regression function is updated by inputting new load data. Simulation results on practical power system show that the online training algorithm results in both smaller number of support vectors and faster convergence speed compared with the conventional SVM regression algorithms.更多还原