基于改进遗传算法的SVR短期电力负载预测

doi:10.3969/j.issn.1006-2475.2016.06.013

计算机与现代化

基于改进遗传算法的SVR短期电力负载预测

南京航空航天大学计算机科学与技术学院,江苏南京210016

收稿日期:2015-11-17 出版日期:2016-06-16 发布日期:2016-06-17
作者简介:杨丹(1991-),女,浙江诸暨人,南京航空航天大学计算机科学与技术学院硕士研究生，研究方向：软件可靠性；臧洌(1964-),女,副教授,硕士,研究方向:网络安全及软件可靠性。

Short-term Electrical Load Forecasting Based on Modified Genetic Algorithm and SVR

ollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Received:2015-11-17 Online:2016-06-16 Published:2016-06-17

摘要/Abstract

摘要： 为了有效且精确地预测电力负载，提出一种基于支持向量回归(Support Vector Regression, SVR)的预测方法对负载消耗进行建模，同时提出一种基于遗传算法(Genetic algorithm, GA)的两级改进遗传算法(Modified Genetic Algorithm, MGA)以调整SVR中的参数。在满足SVR约束条件的情况下选用平均绝对百分比误差(Mean Absolute Percentage Error, MAPE)作为MGA的适应度函数。最后使用一组实际数据对基于MGA的SVR预测方法的可行性和有效性进行了验证。
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关键词: 支持向量回归, 负载预测, 遗传算法

Abstract: In order to effectively and accurately predict the power load, we proposed a Support Vector Regression (SVR) based forecast method to modeling the load consumption, at the same time proposed a two stage modified Genetic Algorithm(MGA) based on Genetic Algorithm to adjust the parameters of SVR. Under the SVR constraint conditions, we chose the Mean Absolute Percentage Error (MAPE) as fitness function of MGA.Finally using a set of practical data, we verified the feasibility and effectiveness of MGA based SVR forecasting method.
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Key words: SVR, load forecasting, GA

中图分类号:

TP311.5

杨丹,臧洌,甘露. 基于改进遗传算法的SVR短期电力负载预测[J]. 计算机与现代化, doi: 10.3969/j.issn.1006-2475.2016.06.013.

YANG Dan, ZANG Lie, GAN Lu. Short-term Electrical Load Forecasting Based on Modified Genetic Algorithm and SVR[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2016.06.013.

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基于改进遗传算法的SVR短期电力负载预测

Short-term Electrical Load Forecasting Based on Modified Genetic Algorithm and SVR

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