摘要
为解决短期电力负荷预测中预测精度差、计算时间长等问题,提出一种基于自组织特征映射网络进行特征提取相似日的极限学习机短期电力负荷预测方法。通过自组织特征映射网络找出与预测日同类型的历史数据作为训练样本;并采用预测能力强、计算时间短的ELM网络进行预测。以某市电力负荷数据进行仿真,并将上述方法与传统神经网络进行对比。仿真算例表明,基于特征提取相似日的ELM方法具有较高的预测精度,泛化性能好,且运算时间短。
In order to solve the problems of forecasting method,such as low forecasting accuracy,and long computation time in short-term electric power load forecasting,an approach to short-term load forecasting based on self-organizing feature mapping of similar days feature extraction and ELM（ Extreme Learning Machine） combined method is proposed in this paper. Firstly,self-organizing neural network is used to the classification of related data.The data of the same type as that of the forecasting day are found out. Then these training samples are forecasted by ELM,which has strong ability to predict and short computing time. The power load data of one city were used for simulating. The proposed method is compared with ELM and back propagation（ BP） neural network. The experimental results show that ELM method based on feature extraction of similar days has high prediction precision,good generalization performance and short running time.
出处
《电子科技》
2015年第12期22-25,共4页
Electronic Science and Technology
基金
国家自然科学基金资助项目(61205076)
上海市研究生创新基金资助项目(JWCXSL1302)
关键词
自组织特征映射
特征提取
相似日
极限学习机
短期负荷预测
self-organizing feature map
feature extraction
similar days
extreme learning machine
short-term load forecasting
