Mamdani模糊系统修正在负荷预测模型中的应用

Kuihe Yang, Lingling Zhao
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引用次数: 8

摘要

采用组合方法建立短期负荷预测模型。该模型既总结了神经网络和模糊系统的优缺点,又考虑了电力系统负荷具有基本负荷分量和变负荷分量的特点。利用神经网络的学习能力来完成电力负荷基本分量的预测工作。神经网络中没有考虑引起负荷变化的其他影响因素。对于受天气、数据类型、节假日等多种因素影响的变异性载荷分量,在模糊逻辑系统中构造隶属函数和模糊规则库,用于修正基本载荷分量。该方法简化了系统结构,提高了预测精度。
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Application of Mamdani Fuzzy System Amendment on Load Forecasting Model
A short-term load forecasting model is adopted with a combined method. The model not only summarizes virtues and defects of neural networks and fuzzy system, but also considers that power system load has characteristics of basic load heft and variability load heft. It uses learned capability of neural networks to complete forecasting work of basic heft for power load. Other effect factors that cause variety of load are unconsidered in neural networks. For variability load heft that is affected by many factors, such as weather, data types and holidays, membership functions and fuzzy rules base are constructed in fuzzy logic system, which is used to correct basic load heft. The method simplifies system structure and enhances forecasting precision.
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