摘要
针对影响连续多日每日最大负荷的因素较多且构成复杂,连续多日负荷预测方法少难度大、含节假日的连续多日负荷预测精度低等问题,分析了近几年工作日电力负荷数据特点,研究了自组织特征映射(Self-Organizing Feature Map,SOM)聚类算法并将其用于负荷数据的预处理,研究了节假日负荷的特性,总结了其负荷变化规律并加以区分预测,提出了一种基于自组织特征映射神经网络的连续多日负荷预测新方法。该方法区分普通工作日与节假日,普通工作日采用自组织特征映射神经网络聚类方法对日最大负荷进行特征提取,建立了以周期特征相似的历史数据作为训练样本的神经网络模型,节假日设定假日影响因子单独预测。运用某市近年的负荷数据进行预测,算例结果显示综合预测误差为3.21%,表明该方法预测精度完全满足实际需求,为连续多日最大负荷预测提供了一种可行的方法。
Aiming at the problem of the factors affecting maximum load in continuous multi-days are complex, hard to forecast the multi-days load, multi-days load including holidays forecasting has low accuracy, characteristics of power load in recent years were analyzed, the Self- Organizing Feature Map cluster method was used in data processing, loading data, characteristics of holidays were studied, a new method of multi-days load forecasting base on Self-Organizing Feature Map（SOM） neural network was proposed. The characteristics of the holiday and the non-holiday daily peak load were investigated respectively. For the non-holiday, the Elman neural network uses the training data which was selected by SOM clustering to forecast. For the holiday, the holiday factor is added. The experimental results show that the prediction accuracy of this method can meet the industry requirement, the average error is 3.21%. It provides a feasible way for multi-day load forecasting.
出处
《机电工程》
CAS
2016年第3期342-346,共5页
Journal of Mechanical & Electrical Engineering
基金
上海张江国家自主创新重点资助项目(201310-PI-B2-008)
关键词
连续多日负荷预测
特征提取
自组织特征映射聚类
日最大负荷
muhi-days load forecasting
feature extraction
Self-Organizing Feature Map clustering
maximum load
