基于支持向量机的电力系统短期负荷预测
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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已有几十年历史,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,研究基于支持向量机的电力系统短期负荷预测方法。
本文全面地总结了支持向量机在短期负荷预测中的应用概况,并从支持向量机的原理出发,对比人工神经网络方法,从本质上阐述了支持向量机方法在短期负荷预测中应用的优越性。与此同时,针对支持向量机在应用中存在一些问题,包括数据预处理、核函数构造及选取、参数优化的方法,做出分析,并归纳了现行的解决方法。特别地,对于一系列支持向量机的改进方法,本文从支持向量机算法用于负荷预测的机理及提高预测精度和速度的角度,全面地进行了归纳,并提出需进一步探讨的关键问题。并结合实例分析各种样本处理情况下基于支持向量机的短期负荷预测的结果。最后,对基于支持向量机的短期负荷预测所需要注意的关键问题做出总结,并提出建议。
鉴于单一预测方法的一些弊端,探索综合预测已经成为学者们的共识。本文采用一种有效的负荷聚类分析处理技术,并将ISODATA聚类算法与支持向量机相结合,首次提出了联合ISODATA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ISODATA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ISODATA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。再次验证了聚类分析在负荷预测中的优势,也证实了运用ISODATA算法对负荷预测数据进行分类的可行性,体现了负荷预测的相似性原则。
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 on load forecasting researches have been developed continuously. As a new technology of data mining, support vector machine has been successfully applied in pattern recognition and regression problem, et al. Toward various factors of non-linear characteristics affecting power, there is a research about the method of short-term load forecasting based on support vector machine by using its advantages of non-linear processing and generating ability.
The application profiles of the support vector machine in the field of short-term load forecasting are comprehensively summarized in this thesis. Starting from the principle of support vector machine and compared with artificial neural network method, the superiorities of the support vector machine method in the application of short-term load forecasting are elaborated. At the same time, some problems about the application of support vector machine, including data pre-processing, the constructing and selection of kernel function, and parameter optimization method, are analyzed in the thesis and the current solutions are provided respectively. In particular, for a series of support vector machine-based improvements and some mixed forecasting methods consisting of support vector machine with other algorithms, a comprehensive summary is given, from the perspective of the mechanism about support vector machine algorithm being applied into load forecasting, and the elevation of prediction accuracy and speed. Meantime, some key issues needing further discussion are put forward. Finally, this thesis summarizes the key issues about short-term load forecasting based on support vector machine, and gives some recommendations.
In view of disadvantages of the single prediction, the exploration for comprehensive prediction has become a consensus among scholars. Therefore this paper adopts an effective ISODATA clustering analysis and process technology for the load data and combines ISODATA clustering algorithm with support vector machine. A new support vector machine method based on ISODATA clustering algorithm for short-term load forecasting is first presented in this thesis. Compared with the conventional support vector machine method, this method chooses training samples by ISODATA 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 application of the proposed method show the usefulness of this method, since both the precision and speed of load forecasting can be improved. The method based on ISODATA clustering algorithm for short-term load forecasting confirms the advantage of Cluster analysis in the load forecasting, also verifies the feasibility of using ISODATA algorithm to classify the load forecast data, and fully embodies the principle of the similarity of load forecasting.
本文全面地总结了支持向量机在短期负荷预测中的应用概况,并从支持向量机的原理出发,对比人工神经网络方法,从本质上阐述了支持向量机方法在短期负荷预测中应用的优越性。与此同时,针对支持向量机在应用中存在一些问题,包括数据预处理、核函数构造及选取、参数优化的方法,做出分析,并归纳了现行的解决方法。特别地,对于一系列支持向量机的改进方法,本文从支持向量机算法用于负荷预测的机理及提高预测精度和速度的角度,全面地进行了归纳,并提出需进一步探讨的关键问题。并结合实例分析各种样本处理情况下基于支持向量机的短期负荷预测的结果。最后,对基于支持向量机的短期负荷预测所需要注意的关键问题做出总结,并提出建议。
鉴于单一预测方法的一些弊端,探索综合预测已经成为学者们的共识。本文采用一种有效的负荷聚类分析处理技术,并将ISODATA聚类算法与支持向量机相结合,首次提出了联合ISODATA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ISODATA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ISODATA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。再次验证了聚类分析在负荷预测中的优势,也证实了运用ISODATA算法对负荷预测数据进行分类的可行性,体现了负荷预测的相似性原则。
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 on load forecasting researches have been developed continuously. As a new technology of data mining, support vector machine has been successfully applied in pattern recognition and regression problem, et al. Toward various factors of non-linear characteristics affecting power, there is a research about the method of short-term load forecasting based on support vector machine by using its advantages of non-linear processing and generating ability.
The application profiles of the support vector machine in the field of short-term load forecasting are comprehensively summarized in this thesis. Starting from the principle of support vector machine and compared with artificial neural network method, the superiorities of the support vector machine method in the application of short-term load forecasting are elaborated. At the same time, some problems about the application of support vector machine, including data pre-processing, the constructing and selection of kernel function, and parameter optimization method, are analyzed in the thesis and the current solutions are provided respectively. In particular, for a series of support vector machine-based improvements and some mixed forecasting methods consisting of support vector machine with other algorithms, a comprehensive summary is given, from the perspective of the mechanism about support vector machine algorithm being applied into load forecasting, and the elevation of prediction accuracy and speed. Meantime, some key issues needing further discussion are put forward. Finally, this thesis summarizes the key issues about short-term load forecasting based on support vector machine, and gives some recommendations.
In view of disadvantages of the single prediction, the exploration for comprehensive prediction has become a consensus among scholars. Therefore this paper adopts an effective ISODATA clustering analysis and process technology for the load data and combines ISODATA clustering algorithm with support vector machine. A new support vector machine method based on ISODATA clustering algorithm for short-term load forecasting is first presented in this thesis. Compared with the conventional support vector machine method, this method chooses training samples by ISODATA 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 application of the proposed method show the usefulness of this method, since both the precision and speed of load forecasting can be improved. The method based on ISODATA clustering algorithm for short-term load forecasting confirms the advantage of Cluster analysis in the load forecasting, also verifies the feasibility of using ISODATA algorithm to classify the load forecast data, and fully embodies the principle of the similarity of load forecasting.
引文
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[3]康重庆、夏清、张伯明.电力系统负荷预测研究综述与发展方向的探讨[J].电力系统自动化,2004
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[9]Chih-Wei Hsu,Chih-Chung Chang,and Chih-Jen Lin. A Practical Guide to Support Vector Classification.Available:http://www.csie.ntu.edu.tw/-cjlin
[10]C.-C.Chang and C.-J.Lin. (2001) LIBSVM:A Library for Support Vector Machines.[Online]. Available:http://www.csie.ntu.edu.tw/-cjlin/libsvm
[11]M.-W.Chang,C.-J.Lin,and R.C.Weng,"Analysis of switching dynamics with competing support vector machines,"in Proc.IJCNN,2002, pp.2387-2392.
[12]Hsuan-Tien Lin,Tzu-Kuo Huang.Grid parameter Search for Regerssion. Software Available:http://www.csie.nut.edu.tw/-cjlin/libsvmtools/
[13]http://neuron-ai.tuke.sk/competition/
[14]http://www.eunite.org/eunite/index.htm
[15]唐杰明、刘俊勇、刘友波.基于最有FCM聚类和最小二乘支持向量机回归的短期电力负荷预测[J].现代电力,2008
[16]Wang H F, Hu D J. Comparison of SVM and LSSVM for Regression [C]. ICNN&B 05 International Conference on Neural Networks and Brain,2005:279-283.
[17]李云飞、黄彦全、蒋功连.基于支持向量机的短期负荷预测的方法改进[J].西华大学学报(自然科学版),2007
[18]赵登福、王蒙、张讲社、王锡凡.基于支撑向量机方法的短期负荷预测[J].中国电机工程学报,2002
[19]李林峰、孙长银.基于FCM聚类与SVM的电力系统短期负荷预测[J].江苏电机工程,2007
[20]姜惠兰、刘晓津、关颖.基于硬C均值聚类算法和支持向量机的电力系统短期负荷预测[J].电网技术,2006
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