【作者】 关颖；

【导师】 姜惠兰；

【作者基本信息】 天津大学 ， 电力系统及其自动化， 2006， 硕士

【摘要】 电力系统短期负荷预测是电力系统安全和经济运行的重要依据。价格竞争机制引入电力系统形成电力市场后,对短期负荷预测的精度和速度提出了更高的要求。虽然负荷预测的研究己有几十年历史,有很多负荷预测的理论和方法,但是随着新理论和新技术的发展,对负荷预测新方法的研究仍在不断地深入进行。支持向量机作为数据挖掘的一项新技术,应用于模式识别和处理回归问题等诸多领域。本文利用支持向量机优越的非线性学习及预测性能,针对短期负荷预测的各种影响因素的非线性特性,提出基于支持向量机的电力系统短期负荷预测新方法,以提高预测精度和时效性,该研究具有重要的理论意义和实用价值。本文分析了支持向量机的基本原理,支持向量机具有非线性拟合、泛化能力强、训练收敛速度快等显著特点。针对电力系统负荷与各种影响因素之间的非线性关系,本文建立了基于支持向量机的短期负荷预测模型,并与神经网络方法作了实例分析比较,结果表明基于支持向量机的负荷预测精度和速度要优于神经网络方法。由于影响负荷的因素繁多且复杂,若对输入不加适当选择处理会导致预测精度降低,训练时间增加。本文采用一种有效的负荷聚类分析处理技术,并将聚类算法与支持向量机相结合,首次提出了联合FCM模糊聚类算法和支持向量机的短期负荷预测新方法。该方法考虑到负荷变化的周期性特点,应用模糊聚类分析的基本原理,依据输入样本的相似度选取训练样本,即选用同类特征数据作为预测输入,保证了数据特征的一致性,强化了历史数据规律。在基于支持向量机负荷预测的基础上,对样本进行模糊聚类分析,选取与预测样本特征相似的样本作为训练样本,建造负荷预测的支持向量机模型。实例分析验证了本文所提方法能够有效地提高负荷预测的精度,缩短了预测时间。更多还原

【Abstract】 Short-term load forecasting provides important foundation for the safety and economical operation of power system. With the fast development of modern electric power systems, the operation of power market requires high precision of short-term load forecasting for the minimal cost of power system operation. Currently there have been more studies in theory and complemented methods of load forecasting and obtained great achievement. New theory and new technology based load forecasting researches have been developed continuously. As new technology of data mining, support vector machines(SVM) have been successfully applied in pattern recognition and regression problem, et al. This paper proposes to use its advantages of non-linear processing and generating ability to accomplish short-term load forecasting of power system, so as to improve forecasting precision and executed speed. Consequently the study is significant in theory and is valuable in practice.This paper analyses the basic theories of SVM. SVM have the remarkable advantages of non-linear regression, high forecasting accuracy and small time complexity. According the non-linear relationship between the forecasting load and its influence factors, this paper proposes a short-term load forecasting model based on SVM. Compared with the forecasting method of artificial neural networks(ANN), the simulation results of the practical application show that the SVM method is much better than ANN.Because of numerous load influence factors having a great of complex characteristics, the pattern, without selecting input vectors, will lead to reduce of the precision and increase of the computering time. Therefore this paper adopts an effective fuzzy clustering analysis and process technology for the load data and combines the clustering algorithm with SVM. A new SVM method based on FCM fuzzy clusting algorithm for short-term load forecasting is first presented in this paper. Compared with the conventional SVM method, this method chooses training samples by fuzzy clustering according to similarity degree of the input samples in consideration of the periodic characteristic of load change, which means take the same type of the data as the learning samples for forecasting, guarantee the consistency of the data characteristic and enhance the history data regulation. The results of the更多还原