| 引用本文: | 熊军华,牛珂,张春歌,李铎,谢飞.基于小波变异果蝇优化支持向量机短期负荷预测方法研究[J].电力系统保护与控制,2017,45(13):71-77.[点击复制] |
| XIONG Junhua,NIU Ke,ZHANG Chunge,LI Duo,XIE Fei.LSSVM in short-term load forecasting based on wavelet transform and mutant fruit fly optimization algorithm[J].Power System Protection and Control,2017,45(13):71-77[点击复制] |
|
| 摘要: |
| 预测精度是电力负荷预测的重要指标。为增强预测精度，提出基于小波变异果蝇优化的支持向量机预测模型(WFOAAM-LSSVM)。利用小波对负荷数据进行预处理，分解成不同尺度的负荷曲线，加强历史数据规律性和随机性。针对果蝇算法寻优精度不高和易陷入局部最优的不足，利用群体适应度方差和当前最优解判断是否陷入局部最优，再进行最优个体扰动和高斯变异操作，对变异后的果蝇个体二次寻优，使支持向量机预测模型精度得到明显增强。利用WFOAAM-LSSVM对2015年河南省某地区历史负荷数据对未来几日预测，并与支持向量机模型以及粒子群优化的支持向量机模型预测结果对比。结果表明:基于小波变异果蝇优化的支持向量机短期负荷预测精度高，具有很好的实际应用意义。 |
| 关键词: 支持向量机 粒子群算法 变异果蝇算法 小波分析 短期负荷预测 |
| DOI：10.7667/PSPC160981 |
| 投稿时间：2016-06-30修订日期：2016-09-26 |
| 基金项目:国家电网公司2016年科技项目“输变电重大工程社会效益评价体系” |
|
| LSSVM in short-term load forecasting based on wavelet transform and mutant fruit fly optimization algorithm |
| XIONG Junhua,NIU Ke,ZHANG Chunge,LI Duo,XIE Fei |
| (College of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China) |
| Abstract: |
| Prediction accuracy is an important index of power load forecasting. In order to enhance the accuracy of prediction, this paper presents support vector machine prediction model based on wavelet transform and the mutant fruit fly parameter optimization algorithm (WFOAAM-LSSVM). The load data are pretreated by wavelet transform, and the load curves are decomposed into different scales, in order to strengthen the regularity and randomness of historical data. In order to overcome the problems of low convergence precision and easily relapsing into local extremum in basic fruit fly optimization algorithm (FOA), and on the condition of basic FOA’s trapping in local extremum judging from the population’s fitness variance and the current optimal, it carries out optimal individual disturbance and Gauss mutation operation and optimizes mutated replicates again to jump out of local extremism and continue to optimize. The accuracy of prediction model is obviously enhanced. The next few days of historical load data of a certain area of Henan Province in 2015 is predicted by using WFOAAM-LSSVM, and the prediction results of support vector machine model and the particle swarm optimization model of support vector machine model are compared. The results show that WFOAAM- LSSVM has high precision in short term load forecasting, and it has a very good practical significance. |
| Key words: support vector machine particle swarm optimization algorithm mutant fruit fly algorithm wavelet analysis short term load forecasting |
