摘要
针对负荷特征一直是实际电力负荷预测中的重大问题。提出了基于粗糙特征量的约简算法。通过对天气及负荷历史数据进行挖掘,找到负荷的关键特征,并与径向基网络结合建立了负荷预测模型。算例结果表明,与按经验选取输入的传统网络相比,预测准确度有了明显的提高,更适用于电力负荷预测。
The key characteristic of mining influence the load is always an important problem in power load forecasting. A reduction algorithm through rough characteristic-component algorithm is introduced. The key characteristics of the date of weather and history load data are discussed,and then a model combined with radical basis function neural network is established. Forecasting results of calculation examples show that the forecasting accuracy is obviously improved and more suitable for short-term load forecasting compared with traditional radical basis function neural network model that chooses input parameters in the light of experience.
出处
《电子科技》
2016年第1期40-43,共4页
Electronic Science and Technology
基金
国家自然科学基金资助项目(6120576)
国家科技部政府间科技合作基金资助项目(2009014)
关键词
电力系统
径向基
粗糙特征量
负荷预测
power system
RBF
rough characteristic-component
load forecasting
