Study of the SMO Algorithm Based on Data Mining in Shot-Term Power Load Forecasting Model

机译：基于数据挖掘在截图功率负荷预测模型中的SMO算法研究

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

A new methodology on the algorithm of sequential minimal optimization for the electric power system load was presented. In order to solve the problem that support vector machines can not deal with large scale data. First, this paper utilizes the advantage of data mining technology in processing large data and eliminating redundant information. The system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features, with this method it can decrease SVR training data; Second, this paper introduces the modified algorithm of sequential minimal optimization (SMO) to increase operational speed by use of a single threshold value. With this method it can decrease SVR training data and overcome the disadvantage of very large data and accelerates processing speed when constructing SVM model. The forecasted results are compared with those SVR employing QP optimization algorithm and BP artificial neural method, and it is shown that the presented forecasting method is more accurate and efficient.

机译：提出了一种新的电力系统负载序列最小优化算法的新方法。为了解决支持向量机无法处理大规模数据的问题。首先，本文利用数据挖掘技术在处理大数据和消除冗余信息时的优势。该系统挖掘具有与预测日相同的气象类别的历史日本负载，以便撰写具有高度相似的气象特征的数据序列，通过这种方法可以减少SVR训练数据;其次，本文介绍了顺序最小优化（SMO）的改进算法，通过使用单个阈值来提高运行速度。通过这种方法，它可以减少SVR训练数据并克服非常大数据的缺点，并在构建SVM模型时加速处理速度。将预测结果与采用QP优化算法和BP人工神经方法的SVR进行比较，并显示出呈现的预测方法更准确，有效。

著录项

来源
《World Congress on Intelligent Control and Automation》|2008年||共6页
会议地点
作者
Dongxiao Niu; Yongli Wang;
展开▼
作者单位

展开▼
会议组织
原文格式 PDF
正文语种
中图分类 TP18-53;
关键词
Support vector machine; Sequential minimal optimization; Data mining; Load forecasted;

机译：支持向量机;顺序最小优化;数据挖掘;负荷预测;

相似文献

外文文献
中文文献
专利

1. Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents [J] . NIU Dong-xiao, WANG Yong-li, MA Xiao-yong 中南大学学报（英文版） . 2010,第002期

机译：基于数据挖掘和李雅普诺夫指数的支持向量机电力负荷预测模型的优化
2. Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimisation: a case study of wind speed time series [J] . Jianzhou Wang, Fanyong Zhang, Feng Liu, Renewable Power Generation, IET . 2016,第3期

机译：基于混合预测模型的数据挖掘和遗传算法自适应粒子群优化：以风速时间序列为例
3. Improved short-term load forecasting model based on data mining technology [J] . Herui Cui, Peng Tian Energy education science and technology . 2012,第2期

机译：基于数据挖掘技术的改进短期负荷预测模型
4. Study of the SMO Algorithm Based on Data Mining in Shot-Term Power Load Forecasting Model [C] . The 7th World Congress on Intelligent Control and Automation(WCICA'08)(第七届智能控制与自动化世界大会)论文集 . 2008

机译：短期电力负荷预测模型中基于数据挖掘的SMO算法研究
5. Studies on information-theoretics based data-sequence pattern-discriminant algorithms: Applications in bioinformatic data mining. [D] . Arredondo, Tomas Vidal. 2003

机译：基于信息理论的数据序列模式判别算法研究：在生物信息数据挖掘中的应用。
6. Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases [O] . Mohsen Hesami, Milad Alizadeh, Roohangiz Naderi, 2020

机译：通过集合模型飞行优化算法预测和优化农杆菌介导的遗传转化：使用菊花数据库的数据挖掘方法
7. A Research on Power Load Forecasting Model Based on Data Mining [O] . Fuyu Sun, Yunshi Yang -1

机译：基于数据挖掘的电力负荷预测模型研究
8. Weather and Load Pattern Correlation Study to Develop a Method of Short-Range Load Forecasting Based on Weather and Load Data [R] . Fillenberg, R. R., Tronvold, J. S. 1965

机译：基于天气和负荷数据的天气和负荷模式相关研究开发短期负荷预测方法

Study of the SMO Algorithm Based on Data Mining in Shot-Term Power Load Forecasting Model

摘要

著录项

相似文献

相关主题

期刊订阅