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Power System Short-term Load Forecasting Based on Fuzzy Neural Network

机译:基于模糊神经网络的电力系统短期负荷预测

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Aiming at short-term load forecasting of power system,considering the factors such as temperature,date type,weather status,etc,which influence the short-term electric load forecasting,a model is set up by dynamic recurrent fuzzy neural network.The fuzzy inference function is realized easily by using a product operation in the network.Introducing local recurrent units to hidden layer,the proposed method can overcome the limit of the traditional BP algorithm.The actual simulation shows that dynamic recurrent fuzzy neural network has advantages of short prediction time,high-precision for forecasting,etc,having a high significance and value.
机译:针对电力系统的短期负荷预测,考虑温度,日期类型,天气状况等因素,影响短期电力负荷的预测,采用动态递归模糊神经网络建立模型。通过在网络中进行乘积运算,很容易实现推理功能。将局部递归单元引入隐藏层,该方法克服了传统BP算法的局限性。实际仿真表明,动态递归模糊神经网络具有预测时间短的优点。时间,高精度的预测等具有很高的意义和价值。

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