一种基于RBF自适应神经模糊推理的短期电力负荷预测方法
Short-Term Load Forecasting Method Based on RBF Adaptive Neural Fuzzy Inference
投稿时间:2018-03-09  修订日期:2018-03-09
DOI:
中文关键词: 短期负荷预测,RBF神经网络,自适应神经模糊推理,MATLAB
英文关键词: short-term  load forecasting, RBF  neural network,Adaptive  Neural Fuzzy  Inference,MATLAB
基金项目:国家留学基金委项目(20175097);河南省科技攻关计划项目(172102210124);河南省高等学校青年骨干教师培养计划项目(2016GGJS-287)
作者单位E-mail
王晓侃 北京交通大学电子信息工程学院 wxkbbg@163.com 
王琼 河南机电职业学院
河南机电职业学院 
 
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中文摘要:
      根据东莞电网的历史负荷数据,分析该地区电力负荷的特征,综合分析天气、温度、日期等因素对电力负荷预测的影响;针对负荷具有一定的客观规律,但又具有很大的随机性和不确定性,提出一种新型基于径向基函数(Radial Basis Function)的自适应神经模糊推理的方法进行短期负荷预测。用MATLAB编制电力系统短期负荷预测程序,并绘制预测结果曲线。结果表明基于RBF自适应神经模糊推理的预测精度是满意的,验证了本方法的有效性和实用性。
英文摘要:
      According to historical load data of Dong Guan grid, by which analyze this area’s power load characteristic and consider the load forecasting influence factors such as the date type, temperature, weather conditions in the first. In view of the load has a certain objective laws, but which has a lot of randomness and uncertainty, applying one kind new based on the RBF (Radial Basis Function) Neural Fuzzy Inference to carry on short-term load forecasting. By programming with MATLAB to carry on short-term power system load forecasting, carry on the short-term load forecast experiment to the Dong Guan grid and draw the forecasting result curves. The result indicated that the RBF Adaptive Neural Fuzzy Inference of the forecast accuracy is satisfied with the verification of this method is effective and practical.
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