摘要
在采用径向基函数神经网络(RBFN)对太阳能发电系统输出功率进行预测的模型中,可以明确日照强度的精度对整个预测系统的精度起到了决定性的作用。通过在RBFN模型中引入模糊规则,改善云量数据的精准度,进而提高预测模型的精度。仿真结果表明,加入了模糊规则的模型,预测曲线更为近似。在全面考虑模糊的基础上,有可能提高预测精度。同时也证明了该方法可用于实际应用。
In the model using Radial Basis Function Neural Network(RBFN)to predict the output power of solar power generation systems,it can be clearly understood that the accuracy of solar radiation intensity has a decisive impact on the accuracy of the entire prediction system.By introducing fuzzy rules into the RBFN model,the accuracy of cloud data was improved,thereby improving the accuracy of the prediction model.The simulation results show that the prediction curve is more approximate with the addition of fuzzy rules to the model.Therefore,based on the comprehensive application of fuzzy rules,the prediction accuracy will be improved,which also proves that this method can be used in practical use.
作者
华龙
齐冲
刘雪娇
HUA Long;QI Chong;LIU Xuejiao(Beijing Metro Operation Administration Co.Ltd.,Beijing 100070,China;Communication and Signaling Branch Company Affiliated with Beijing Mass Transit Railway Operation Co.Ltd.,Beijing 100082,China)
出处
《科技和产业》
2023年第24期63-67,共5页
Science Technology and Industry
关键词
模糊规则
预测
日照强度
神经网络
fuzzy rule
prediction
solar radiation intensity
neural network