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A Short-term Load Forecasting Method Based on Fuzzy Neural RBF Network Adaptive Control

机译:基于模糊神经RBF网络自适应控制的短期负荷预测方法

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According to historical load data of the power grid in a certain area, 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 practical 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.
机译:根据一定区域的电网的历史载荷数据,通过其分析该区域的电力负荷特性,并考虑第一个负载预测影响因素,例如日期类型,温度,天气条件。鉴于负荷具有一定的客观法,但这具有大量的随机性和不确定性,基于RBF(径向基函数)神经模糊推理来延长短期负荷预测。通过使用MATLAB进行编程来进行短期电力系统负荷预测,对实际网格进行短期负荷预测实验,并绘制预测结果曲线。结果表明,预测精度的RBF自适应神经模糊推理对该方法的验证感到满意,这是有效且实用的。

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