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Using adaptive network based fuzzy inference system to forecast regional electricity loads

机译：基于自适应网络的模糊推理系统预测区域用电

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摘要

Since accurate regional load forecasting is very important for improvement of the management performance of the electric industry, various regional load forecasting methods have been developed. The purpose of this study is to apply the adaptive network based fuzzy inference system (ANFIS) model to forecast the regional electricity loads in Taiwan and demonstrate the forecasting performance of this model. Based on the mean absolute percentage errors and statistical results, we can see that the ANFIS model has better forecasting performance than the regression model, artificial neural network (ANN) model, support vector machines with genetic algorithms (SVMG) model, recurrent support vector machines with genetic algorithms (RSVMG) model and hybrid ellipsoidal fuzzy systems for time series forecasting (HEFST) model. Thus, the ANFIS model is a promising alternative for forecasting regional electricity loads.
机译：由于准确的区域负荷预测对于提高电力行业的管理绩效非常重要，因此已经开发了各种区域负荷预测方法。这项研究的目的是应用基于自适应网络的模糊推理系统（ANFIS）模型来预测台湾地区的电力负荷并证明该模型的预测性能。根据平均绝对百分比误差和统计结果，我们可以看到ANFIS模型的预测性能优于回归模型，人工神经网络（ANN）模型，带有遗传算法的支持向量机（SVMG）模型，递归支持向量机使用遗传算法（RSVMG）模型和混合椭圆体模糊系统进行时间序列预测（HEFST）模型。因此，ANFIS模型是预测区域电力负荷的有希望的替代方法。

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